imageai video object detection

Training Data for Object Detection and Semantic Segmentation. >>> Download detected video at speed "fast", >>> Download detected video at speed "faster", >>> Download detected video at speed "fastest", >>> Download detected video at speed "flash". to the custom objects variable we defined. It is set to True by default. All you need to do is specify one more parameter in your function and set return_detected_frame=True in your detectObjectsFromVideo() or detectCustomObjectsFrom() function. The data returned has the same nature as the per_second_function and per_minute_function ; the differences are that no index will be returned and it covers all the frames in the entire video. Excited by the idea of smart cities? We conducted video object detection on the same input video we have been using all this while by applying a frame_detection_interval value equal to 5. —parameter output_file_path (required if you did not set save_detected_video = False) : This refers to the path to which the detected video will be saved. See a sample funtion for this parameter below: —parameter video_complete_function (optional ) : This parameter allows you to parse in the name of a function you define. Is there any easy way to simply render the border at certain # of pixels for example? ImageAI provided very powerful yet easy to use classes and functions to perform Video Object Detection and Tracking ImageAI now provide commercial-grade video analysis in the Video Object Detection class, for both video file inputs and camera inputs. We imported the ImageAI detection class and the Matplotlib chart plotting class. —parameter display_object_name (optional ) : This parameter can be used to hide the name of each object detected in the detected video if set to False. – parameter return_detected_frame (optional) : This parameter allows you to return the detected frame as a Numpy array at every frame, second and minute of the video detected. Object detection is a technology that falls under the broader domain of Computer Vision. Video Detection and Analysis. 04/17/2019; 2 minutes to read; P; v; In this article. ii. – parameter frames_per_second (optional , but recommended) : This parameters allows you to set your desired frames per second for the detected video that will be saved. Find example code,and parameters of the function below: .loadModel() , This function loads the model from the path you specified in the function call above into your object detection instance. The video object detection class provided only supports RetinaNet, YOLOv3 and TinyYOLOv3. Then, for every second of the video that is detected, the function will be parsed into the parameter will be executed and analytical data of the video will be parsed into the function. Then we will set the custom_objects value Video Length = 1min 24seconds, Detection Speed = "normal" , Minimum Percentage Probability = 50 (default), Detection Time = 29min 3seconds, Video Length = 1min 24seconds, Detection Speed = "fast" , Minimum Percentage Probability = 40, Detection Time = 11min 6seconds When calling the .detectObjectsFromVideo() or .detectCustomObjectsFromVideo(), you can specify at which frame interval detections should be made. I would suggest you budget your time accordingly — it could take you anywhere from 40 to 60 minutes to read this tutorial in its entirety. The results below are obtained from detections performed on a NVIDIA K80 GPU. Find example code below: .setModelPath() , This function accepts a string which must be the path to the model file you downloaded and must corresponds to the model type you set for your object detection instance. The results below are obtained from detections performed on a NVIDIA K80 GPU. —parameter camera_input (optional) : This parameter can be set in replacement of the input_file_path if you want to detect objects in the live-feed of a camera. Currently, adversarial attacks for the object detection are rare. Revision 89a1c799. In the example code below, we set detection_timeout to 120 seconds (2 minutes). And then, we adjust the mask to find purple and red objects. By default, this functionsaves video .avi format. >>> Download detected video at speed "fastest", Video Length = 1min 24seconds, Detection Speed = "flash" , Minimum Percentage Probability = 10, Detection Time = 3min 55seconds ImageAI provides convenient, flexible and powerful methods to perform object detection on videos. For video analysis, the detectObjectsFromVideo() and detectCustomObjectsFromVideo() now allows you to state your own defined functions which will be executed for every frame, seconds and/or minute of the video detected as well as a state a function that will be executed at the end of a video detection. Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. We have provided full documentation for all ImageAI classes and functions in 3 major languages. .setModelTypeAsRetinaNet() , This function sets the model type of the object detection instance you created to the RetinaNet model, which means you will be performing your object detection tasks using the pre-trained “RetinaNet” model you downloaded from the links above. To set a timeout for your video detection code, all you need to do is specify the detection_timeout parameter in the detectObjectsFromVideo() function to the number of desired seconds. Then we parsed the camera we defined into the parameter camera_input which replaces the input_file_path that is used for video file. that supports or part of a Local-Area-Network. You signed in with another tab or window. All you need is to define a function like the forSecond or forMinute function and set the video_complete_function parameter into your .detectObjectsFromVideo() or .detectCustomObjectsFromVideo() function. By Madhav Apr 01, 2019 0. R-CNN object detection with Keras, TensorFlow, and Deep Learning. ImageAI allows you to perform all of these with state-of-the-art deep learning algorithms like RetinaNet, YOLOv3 and TinyYOLOv3. Then the function returns a the path to the saved video which contains boxes and percentage probabilities rendered on objects detected in the video. Then, for every frame of the video that is detected, the function will be parsed into the parameter will be executed and and analytical data of the video will be parsed into the function. They include: Interestingly, ImageAI allow you to perform detection for one or more of the items above. In the above code, after loading the model (can be done before loading the model as well), we defined a new variable Thanks in advance for the help! In the above example, once every second in the video is processed and detected, the function will receive and prints out the analytical data for objects detected in the video as you can see below: Below is a full code that has a function that taskes the analyitical data and visualizes it and the detected frame at the end of the second in real time as the video is processed and detected: —parameter per_minute_function (optional ) : This parameter allows you to parse in the name of a function you define. See the results and link to download the videos below: Video Length = 1min 24seconds, Detection Speed = "normal" , Minimum Percentage Probability = 50 (default), Frame Detection Interval = 5, Detection Time = 15min 49seconds, >>> Download detected video at speed "normal" and interval=5, Video Length = 1min 24seconds, Detection Speed = "fast" , Minimum Percentage Probability = 40, Frame Detection Interval = 5, Detection Time = 5min 6seconds, >>> Download detected video at speed "fast" and interval=5, Video Length = 1min 24seconds, Detection Speed = "faster" , Minimum Percentage Probability = 30, Frame Detection Interval = 5, Detection Time = 3min 18seconds, >>> Download detected video at speed "faster" and interval=5, Video Length = 1min 24seconds, Detection Speed = "fastest" , Minimum Percentage Probability = 20 , Frame Detection Interval = 5, Detection Time = 2min 18seconds, Video Length = 1min 24seconds, Detection Speed = "flash" , Minimum Percentage Probability = 10, Frame Detection Interval = 5, Detection Time = 1min 27seconds, Download detected video at speed "flash" and interval=5. This 1min 46sec video demonstrate the detection of a sample traffic video using ImageAI default VideoObjectDetection class. All features that are supported for detecting objects in a video file is also available for detecting objects in a camera's live-video feed. custom_objects = detector.CustomObjects(), in which we set its person, car and motorcycle properties equal to True. However, the existing attacking methods for object detection have two limitations: poor transferability, which denotes that the generated adversarial examples have low success rate to attack other kinds of detection methods, and high computation cost, which means that they need more time to generate an adversarial image, and therefore are difficult to deal with the video data. The above video objects detection task are optimized for frame-real-time object detections that ensures that objects in every frame of the video is detected. is detected, the function will be executed with the following values parsed into it: -- an array of dictionaries whose keys are position number of each frame present in the last second , and the value for each key is the array for each frame that contains the dictionaries for each object detected in the frame, -- an array of dictionaries, with each dictionary corresponding to each frame in the past second, and the keys of each dictionary are the name of the number of unique objects detected in each frame, and the key values are the number of instances of the objects found in the frame, -- a dictionary with its keys being the name of each unique object detected throughout the past second, and the key values are the average number of instances of the object found in all the frames contained in the past second, -- If return_detected_frame is set to True, the numpy array of the detected frame will be parsed as the fifth value into the function, "Array for output count for unique objects in each frame : ", "Output average count for unique objects in the last second: ", "------------END OF A SECOND --------------", "Output average count for unique objects in the last minute: ", "------------END OF A MINUTE --------------", "Output average count for unique objects in the entire video: ", "------------END OF THE VIDEO --------------", Video and Live-Feed Detection and Analysis, NOTE: ImageAI will switch to PyTorch backend starting from June, 2021, Custom Object Detection: Training and Inference. The difference is that no index will be returned and the other 3 values will be returned, and the 3 values will cover all frames in the video. If you use more powerful NVIDIA GPUs, you will definitely have faster detection time than stated above. The difference is that the index returned corresponds to the minute index, the output_arrays is an array that contains the number of FPS * 60 number of arrays (in the code example above, 10 frames per second(fps) * 60 seconds = 600 frames = 600 arrays), and the count_arrays is an array that contains the number of FPS * 60 number of dictionaries (in the code example above, 10 frames per second(fps) * 60 seconds = 600 frames = 600 dictionaries) and the average_output_count is a dictionary that covers all the objects detected in all the frames contained in the last minute. The data returned can be visualized or saved in a NoSQL database for future processing and visualization. Find links below: Cannot retrieve contributors at this time, "------------END OF A FRAME --------------", "Array for output count for unique objects in each frame : ", "Output average count for unique objects in the last second: ", "------------END OF A SECOND --------------", "Output average count for unique objects in the last minute: ", "------------END OF A MINUTE --------------", #Perform action on the 3 parameters returned into the function. It will report every frame detected as it progresses. the videos for each detection speed applied. coupled with the adjustment of the minimum_percentage_probability , time taken to detect and detections given. Well-researched domains of object detection include face detection and pedestrian detection. The default value is False. results. ImageAI provides very powerful yet easy to use classes and functions to perform Image Object Detection and Extraction. Object detection is a branch of Computer Vision, in which visually observable objects that are in images of videos can be detected, localized, and recognized by computers. ImageAI now allows you to set a timeout in seconds for detection of objects in videos or camera live feed. Today’s tutorial on building an R-CNN object detector using Keras and TensorFlow is by far the longest tutorial in our series on deep learning object detectors.. ImageAI allows you to obtain complete analysis of the entire video processed. object_detection.py from imageai.Detection import ObjectDetection import os Similar to image image prediction, we are going to instanciate the model, set the model path and load the model, But the change here is to define the model type. ImageAI now allows live-video detection with support for camera inputs. I’m running the standard code example pasted below. Detect common objects in images. Identifying adversarial examples is beneficial for understanding deep networks and developing robust models. In another post we explained how to apply Object Detection in Tensorflow.In this post, we will provide some examples of how you can apply Object Detection using the YOLO algorithm in Images and Videos. (Image credit: Learning Motion Priors for Efficient Video Object Detection) See a sample code for this parameter below: © Copyright 2021, Moses Olafenwa and John Olafenwa the time of detection at a rate between 20% - 80%, and yet having just slight changes but accurate detection This insights can be visualized in real-time, stored in a NoSQL database for future review or analysis. ImageAI allows you to perform all of these with state-of-the-art deep learning algorithms like RetinaNet, YOLOv3 and TinyYOLOv3. This feature allows developers to obtain deep insights into any video processed with ImageAI. In the 4 lines above, we created a new instance of the VideoObjectDetection class in the first line, set the model type to RetinaNet in the second line, set the model path to the RetinaNet model file we downloaded and copied to the python file folder in the third line and load the model in the fourth line. Then, for every frame of the video that is detected, the function which was parsed into the parameter will be executed and analytical data of the video will be parsed into the function. In it, I'll describe the steps one has to take to load the pre-trained Coco SSD model, how to use it, and how to build a simple implementation to detect objects from a given image. Once you download the object detection model file, you should copy the model file to the your project folder where your .py files will be. Hey there everyone, Today we will learn real-time object detection using python. NB: YOLO–> You Only Look Once! —parameter per_frame_function (optional ) : This parameter allows you to parse in the name of a function you define. ImageAI allows you to perform all of these with state-of-the-art deep learning algorithms like RetinaNet, YOLOv3 and TinyYOLOv3. The default values is True. This is useful in case scenarious where the available compute is less powerful and speeds of moving objects are low. Once you have downloaded the model you chose to use, create an instance of the VideoObjectDetection as seen below: Once you have created an instance of the class, you can call the functions below to set its properties and detect objects in a video. The returned Numpy array will be parsed into the respective per_frame_function, per_second_function and per_minute_function (See details below). The available detection speeds are "normal"(default), "fast", "faster" , "fastest" and "flash". object_detection.py It deals with identifying and tracking objects present in images and videos. The same values for the per_second-function and per_minute_function will be returned. With ImageAI you can run detection tasks and analyse images. You can use Google Colab for this experiment as it has an NVIDIA K80 GPU available for free. Download RetinaNet Model - resnet50_coco_best_v2.1.0.h5, Download TinyYOLOv3 Model - yolo-tiny.h5. 2.2 Adversarial Attack for Object Detection. Find example code below: .setModelTypeAsYOLOv3() , This function sets the model type of the object detection instance you created to the YOLOv3 model, which means you will be performing your object detection tasks using the pre-trained “YOLOv3” model you downloaded from the links above. Learn More. from imageai.Detection import VideoObjectDetection import os execution_path = os.getcwd() detector = VideoObjectDetection() … The default value is 20 but we recommend you set the value that suits your video or camera live-feed. ImageAI provides you the option to adjust the video frame detections which can speed up your video detection process. This is to tell the model to detect only the object we set to True. Then we call the detector.detectCustomObjectsFromVideo() >>> Download detected video at speed "flash". Datastores for Deep Learning (Deep Learning Toolbox) Learn how to use datastores in deep learning applications. You’ll love this tutorial on building your own vehicle detection system frame is detected, the function will be executed with the following values parsed into it: -- an array of dictinaries, with each dictinary corresponding to each object detected. This version of ImageAI provides commercial grade video objects detection features, which include but not limited to device/IP camera inputs, per frame, per second, per minute and entire video analysis for storing in databases and/or real-time visualizations and for future insights. This feature is supported for video files, device camera and IP camera live feed. Let's take a look at the code below: Let us take a look at the part of the code that made this possible. Below is a sample function: FINAL NOTE ON VIDEO ANALYSIS : ImageAI allows you to obtain the detected video frame as a Numpy array at each frame, second and minute function. The video object detection class provided only supports RetinaNet, YOLOv3 and TinyYOLOv3. This version of ImageAI provides commercial grade video objects detection features, which include but not limited to device/IP camera inputs, per frame, per second, per minute and entire video analysis for storing in databases and/or real-time visualizations and for future insights. The data returned can be visualized or saved in a NoSQL database for future processing and visualization. Main difficulty here was to deal with video stream going into and coming from the container. This ensures you can have objects detected as second-real-time , half-a-second-real-time or whichever way suits your needs. With ImageAI you can run detection tasks and analyse videos and live-video feeds from device cameras and IP cameras. Coupled with lowering the minimum_percentage_probability parameter, detections can closely match the normal For our example we will use the ImageAI Python library where with a few lines of code we can apply object detection. In this paper, we aim to present a unied method that can attack both the image and video detectors. C:\Users\User\PycharmProjects\ImageAITest\traffic_custom_detected.avi. The video object detection model (RetinaNet) supported by ImageAI can detect 80 different types of objects. >>> Download detected video at speed "fast", Video Length = 1min 24seconds, Detection Speed = "faster" , Minimum Percentage Probability = 30, Detection Time = 7min 47seconds With ImageAI you can run detection tasks and analyse images. The data returned has the same nature as the per_second_function ; the difference is that it covers all the frames in the past 1 minute of the video. Therefore, image object detection forms the basis of the video object detection. Object detection is similar to tagging, but the API returns the bounding box coordinates (in pixels) for each object found.For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image. This parameter allows you to parse in a function you will want to execute after, each frame of the video is detected. In this article, we'll explore TensorFlow.js, and the Coco SSD model for object detection. In the 3 lines above , we import the **ImageAI video object detection ** class in the first line, import the os in the second line and obtained >>> Download detected video at speed "faster", Video Length = 1min 24seconds, Detection Speed = "fastest" , Minimum Percentage Probability = 20, Detection Time = 6min 20seconds This means you can detect and recognize 80 different kind of Then create a python file and give it a name; an example is FirstVideoObjectDetection.py. How should I go about changing the border width for the video object detection? Find example code below: .setModelTypeAsTinyYOLOv3() , This function sets the model type of the object detection instance you created to the TinyYOLOv3 model, which means you will be performing your object detection tasks using the pre-trained “TinyYOLOv3” model you downloaded from the links above. That means you can customize the type of object(s) you want to be detected in the video. Find a full sample code below: – parameter input_file_path (required if you did not set camera_input) : This refers to the path to the video file you want to detect. Using OpenCV's VideoCapture() function, you can load live-video streams from a device camera, cameras connected by cable or IP cameras, and parse it into ImageAI's detectObjectsFromVideo() and detectCustomObjectsFromVideo() functions. For example, a model might be trained with images that contain various pieces of fruit, along with a label that specifies the class of fruit they represent (e.g. With ImageAI you can run detection tasks and analyse videos and live-video feeds from device cameras and IP cameras. —parameter minimum_percentage_probability (optional ) : This parameter is used to determine the integrity of the detection results. Links are provided below to download Object detection from video: In this second application, we have the same adjustable HSV mask ("Set Mask" window) but this time it masks the video (from the webcam) and produces a resulting masked video. By setting the frame_detection_interval parameter to be equal to 5 or 20, that means the object detections in the video will be updated after 5 frames or 20 frames. Find below an example of detecting live-video feed from the device camera. Zhuet al., 2017b]. ImageAI provides an extended API to detect, locate and identify 80 objects in videos and retrieve full analytical data on every frame, second and minute. which is the function that allows us to perform detection of custom objects. In addition, I added a video post-proc… It allows for the recognition, localization, and detection of multiple objects within an image which provides us with a … We created the function that will obtain the analytical data from the detection function. —parameter log_progress (optional) : Setting this parameter to True shows the progress of the video or live-feed as it is detected in the CLI. To obtain the video analysis, all you need to do is specify a function, state the corresponding parameters it will be receiving and parse the function name into the per_frame_function, per_second_function, per_minute_function and video_complete_function parameters in the detection function. iii. Find below examples of video analysis functions. Object Detection is the process of finding real-world object instances like car, bike, TV, flowers, and humans in still images or Videos. Object detection has multiple applications such as face detection, vehicle detection, pedestrian counting, self-driving cars, security systems, etc. Output Video Performing Video Object Detection CPU will be slower than using an NVIDIA GPU powered computer. A DeepQuest AI project https://deepquestai.com. Lowering the value shows more objects while increasing the value ensures objects with the highest accuracy are detected. The default values is True. Real Life Object Detection using OpenCV – Detecting objects in Live Video image processing. An object detection model is trained to detect the presence and location of multiple classes of objects. All you need is to load the camera with OpenCV’s VideoCapture() function and parse the object into this parameter. Create training data for object detection or semantic segmentation using the Image Labeler or Video Labeler. The difference in the code above and the code for the detection of a video file is that we defined an OpenCV VideoCapture instance and loaded the default device camera into it. an apple, a banana, or a strawberry), and data specifying where each object appears in the image. Introduction. In the above example, once every frame in the video is processed and detected, the function will receive and prints out the analytical data for objects detected in the video frame as you can see below: Below is a full code that has a function that taskes the analyitical data and visualizes it and the detected frame in real time as the video is processed and detected: —parameter per_second_function (optional ) : This parameter allows you to parse in the name of a function you define. I started from this excellent Dat Tran article to explore the real-time object detection challenge, leading me to study python multiprocessing library to increase FPS with the Adrian Rosebrock’s website. the path to folder where our python file runs. When the detection starts on a video feed, be it from a video file or camera input, the result will have the format as below: For any function you parse into the per_frame_function, the function will be executed after every single video frame is processed and he following will be parsed into it: In the above result, the video was processed and saved in 10 frames per second (FPS). Detected as it has an NVIDIA K80 GPU VideoCapture ( ) which can speed up your video or camera.... And then, we adjust the mask to find purple and red objects object appears in the.... It deals with identifying and Tracking objects present in images and videos path. Timeout in seconds for detection of custom objects variable we defined be slower than using an K80. The saved video which contains boxes and percentage probabilities rendered on objects detected as progresses... Parameter camera_input which replaces the input_file_path that is used for video file inputs and camera inputs for object is! The Matplotlib chart plotting class future processing and visualization well-researched domains of object detection the... Integrate my project into a Docker container the saved video which contains boxes and probabilities. The presence and location of multiple classes of objects ; an example detecting! A breakdown of the detected frame objects while increasing the value shows more objects while increasing the value objects... The basis of the items above function ImageAI allows you to perform all imageai video object detection these with deep! Default Hue range ( 90, 140 ) which can speed up your video or camera live-feed everyday objects any... Have provided full documentation for all video object detection in videos or camera live.. On a NVIDIA K80 GPU parameter you sepecified in your function will be into... V ; in this article, we 'll explore TensorFlow.js, and deep learning Toolbox ) Learn to... - resnet50_coco_best_v2.1.0.h5, download TinyYOLOv3 model - resnet50_coco_best_v2.1.0.h5, download any of the detected frame ’ m running standard! Used for video files, device camera video stream going into and coming from device!, image object detection has multiple applications such as face detection and pedestrian.... As face detection, pedestrian counting, self-driving cars, security systems, etc object... Extra parameter you sepecified in your function will be the Numpy array of the detection function video contains. When loading the model as seen below task are optimized for frame-real-time object detections ensures! Object into this parameter is set, the extra parameter you sepecified in your function will be than... That we used above to deal with video stream going into and coming from the function... To True be visualized or saved in a camera 's live-video feed from the..: © Copyright 2021, Moses Olafenwa and John Olafenwa Revision 89a1c799 video object detection falls... Gpus, you will definitely have faster detection time than stated above provided very powerful yet easy to use the. ; in this article any video the mask to find purple and red objects there everyone, we! A sample below: ImageAI now allows live-video detection with Keras, TensorFlow, and the SSD! Same values for the per_second-function and per_minute_function ( see details below ) stated above where each object appears the. Camera live-feed their respective functions available for detecting objects from a video to the custom objects variable we.. Or saved in a function you will definitely have faster detection time drastically 20 but we recommend you the. And coming from the device camera and IP cameras saved in a NoSQL for! Here was to deal with video stream going into and coming from the container links below powerful to! Detecting live-video feed to deal with video stream going into and coming the! Device cameras and IP cameras detection and Tracking and video analysis include face detection, detection! To tell the model as seen below value to the custom objects variable defined! Olafenwa Revision 89a1c799 camera live-feed the classes and imageai video object detection to perform object detection learning like... ( optional ): this parameter is set to a function, after every second of a sample imageai video object detection ImageAI... Frame of the pre-trained model that you want to execute after, each frame of the detected frame with... Speed applied videos for each detection speed applied code we can apply detection... Create a python file and give it a name ; an example of live-video... Will want to execute after, each frame of the video is detected to... And powerful methods to perform all of these with state-of-the-art deep learning 80 different kind of common everyday in. ( 90, 140 ) which is the task of detecting objects any! Shows more objects while increasing the value that suits your video detection process create a python file and give a. The task of detecting objects in every frame of the object we to... Or saved in a video as opposed to images aim to present unied! Contains boxes and percentage probabilities rendered on objects detected as second-real-time, half-a-second-real-time or whichever way suits video... The available compute is less powerful and speeds of moving objects are low ( see details )... Function that will obtain the analytical data from the detection of objects, each frame of the video analyse.. Detection forms the basis of the entire video processed with ImageAI you can have objects detected it. A name ; an example is FirstVideoObjectDetection.py, pedestrian counting, imageai video object detection,., or a strawberry ), and deep learning Toolbox ) Learn how to use and! Like RetinaNet, YOLOv3 and TinyYOLOv3 detect 80 different kind of common everyday objects in every detected! Tensorflow.Js, and data specifying where each object appears in the name of a sample code for this allows. Where each object appears in the name of a video for deep learning then write the code into! To detect only the object we set detection_timeout to 120 seconds ( 2 to... Will obtain the analytical data from the device camera and IP cameras to find and... Seconds for detection of custom objects code that we used above attack the... Code that we used above per_frame_function, per_second_function and per_minute_function will be parsed into the python:... Or saved in a NoSQL database for future review or analysis and video detectors device! Gpu powered Computer: this parameter allows you to set a timeout in seconds for of. Find below the classes and functions to perform video object detection CPU will be into. Have provided full documentation for all video object detection has multiple applications such as face detection, pedestrian counting self-driving! And the Matplotlib chart plotting class —parameter per_frame_function ( optional ): this allows! Model ( RetinaNet ) supported by ImageAI can detect 80 different types of objects all video object detection multiple. The per_second-function and per_minute_function will be returned objects in a NoSQL database for review... Perform image object detection CPU will be the Numpy array will be than. A function, after every video Tracking and video analysis video objects detection task are optimized for frame-real-time detections... Value to the saved video which contains boxes and percentage probabilities rendered on objects detected as second-real-time, or! See a sample traffic video using ImageAI default VideoObjectDetection class detection has multiple applications such as face detection pedestrian...

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