Main Article Content
Occlusion in people tracking in computer vision is the problem that affects the tracking continuity as the information of the object is lost when the tracked object is behind another object. This study proposes a tracking algorithm that is robust to occlusion. The algorithm is designed based on the algorithm called Simple
Online and Real-time Tracking (SORT) which utilizes deep neural network detector, Kalman Filter, and Hungarian algorithm. The feature extraction method is used to capture the information of objects before and after occlusion to solve the problem of multi-object tracking in the presence of occlusion. This method can improve the lack of memory bottleneck of SORT which is a crucial requirement for robust multi-object tracking. The
experiment is performed on 13 testing videos which contain multi-people walking pass each other with and without background noise. The result from the experiment shows that the proposed method can increase multi-object tracking accuracy of SORT. The algorithm can correctly re-identify the object after the occlusion event.
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