Less false detections, fewer identity switches: methods for the improvement of deep sort

Ziming Huang; Ke Tan; Zhenzhong Wei

doi:10.1117/12.2612162

19 November 2021 Less false detections, fewer identity switches: methods for the improvement of deep sort

Ziming Huang, Ke Tan, Zhenzhong Wei

Proceedings Volume 12059, Tenth International Symposium on Precision Mechanical Measurements; 120591D (2021) https://doi.org/10.1117/12.2612162
Event: Tenth International Symposium on Precision Mechanical Measurements, 2021, Qingdao, China

Abstract

Deep sort algorithm is a multi-object tracking algorithm with high tracking accuracy and speed. However, due to the lack of detection filter and the association stage of a single frame, the accuracy of multi-object tracking is remaining enhancement. In this paper, we propose a DO-Adaptive NMS algorithm to filter the detections, and combine the K nearest neighbor algorithm with the intersection of union algorithm to sharpen features of the trajectories. Besides, we put forward a weighted algorithm of motion information and appearance information, which takes the disappear time of trajectories into consideration. Experiments show that the methods mentioned above all perform better than the original algorithm.

Citation Download Citation

Ziming Huang, Ke Tan, and Zhenzhong Wei "Less false detections, fewer identity switches: methods for the improvement of deep sort", Proc. SPIE 12059, Tenth International Symposium on Precision Mechanical Measurements, 120591D (19 November 2021); https://doi.org/10.1117/12.2612162

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