Abstract
The human brain can effortlessly perform vision processes using the visual system, which helps solve multi-object tracking (MOT) problems. However, few algorithms simulate human strategies for solving MOT. Therefore, devising a method that simulates human activity in vision has become a good choice for improving MOT results, especially occlusion. Eight brain strategies have been studied from a cognitive perspective and imitated to build a novel algorithm. Two of these strategies gave our algorithm novel and outstanding results, rescuing saccades and stimulus attributes. First, rescue saccades were imitated by detecting the occlusion state in each frame, representing the critical situation that the human brain saccades toward. Then, stimulus attributes were mimicked by using semantic attributes to reidentify the person in these occlusion states. Our algorithm favourably performs on the MOT17 dataset compared to state-of-the-art trackers. In addition, we created a new dataset of 40,000 images, 190,000 annotations and 4 classes to train the detection model to detect occlusion and semantic attributes. The experimental results demonstrate that our new dataset achieves an outstanding performance on the scaled YOLOv4 detection model by achieving a 0.89 mAP 0.5.
Highlights
IntroductionObject tracking is of great interest to researchers because of its numerous computer vision applications such as robot navigation, self-driving, and smart surveillance
We introduced a metric called intersection over attribute intersection over union (IOU), which only indicates the overlap between Bb (IOA); this attribute here could be a shirt or trouser
This paper has presented a novel multi-object tracking (MOT) algorithm that imitates the human brain in eight different areas, and a new dataset of 40,000 images and more than 190,000 annotations to serve the computer vision community
Summary
Object tracking is of great interest to researchers because of its numerous computer vision applications such as robot navigation, self-driving, and smart surveillance. It attempts to give a unique ID for every object throughout all frames. Researchers have developed robust algorithms to address these challenges Their methods, have not solved one of the main problems in MOT, which is occlusion. Their algorithms have not reached human brain performance because not many have studied how the human brain deals with such challenges
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