TY - GEN
T1 - Multi-Object Tracking with Adaptive Cost Matrix
AU - Wang, Mingyan
AU - Lit, Bozheng
AU - Jiang, Haoran
AU - Zhang, Junjie
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Multi-object tracking (MOT) aims at detecting and assigning identities for objects in videos. Complicated scenes, severe occlusions, irregular motions, and ambiguous appearances of objects hinder the further advance, which occurs frequently in pedestrian tracking. To tackle these challenges, we present a simple yet effective MOT framework focusing on two-fold: a more robust motion feature and a proper association paradigm. The Hybrid Motion Feature (HMF) integrates Intersection over Union (IoU), Euclidean distance metric, and area ratio information, and the latter two resort to the historical average variations to improve the cost matrix construction. Moreover, the new association paradigm, namely the Adaptive Calculation Method (ACM) performs better by avoiding the manual weighting of the motion and appearance-based cost matrix. In addition, the new correction method, i.e., reinitializing the state of the Kalman Filter when a severe mismatch occurs between the ground truth and the predicted trajectory, mitigates the effect of irregular motions. We achieved 80.7 MOTA, 78.5 IDF1, and 64.0 HOTA, outperforming the state-of-the-art model ByteTrack on the public MOT17 benchmark.
AB - Multi-object tracking (MOT) aims at detecting and assigning identities for objects in videos. Complicated scenes, severe occlusions, irregular motions, and ambiguous appearances of objects hinder the further advance, which occurs frequently in pedestrian tracking. To tackle these challenges, we present a simple yet effective MOT framework focusing on two-fold: a more robust motion feature and a proper association paradigm. The Hybrid Motion Feature (HMF) integrates Intersection over Union (IoU), Euclidean distance metric, and area ratio information, and the latter two resort to the historical average variations to improve the cost matrix construction. Moreover, the new association paradigm, namely the Adaptive Calculation Method (ACM) performs better by avoiding the manual weighting of the motion and appearance-based cost matrix. In addition, the new correction method, i.e., reinitializing the state of the Kalman Filter when a severe mismatch occurs between the ground truth and the predicted trajectory, mitigates the effect of irregular motions. We achieved 80.7 MOTA, 78.5 IDF1, and 64.0 HOTA, outperforming the state-of-the-art model ByteTrack on the public MOT17 benchmark.
KW - Appearance Feature
KW - Cost Matrix
KW - Data Association
KW - Hybrid Motion Feature
KW - Kalman Filter
KW - Multi-object Tracking
UR - http://www.scopus.com/inward/record.url?scp=85143622319&partnerID=8YFLogxK
U2 - 10.1109/MMSP55362.2022.9948977
DO - 10.1109/MMSP55362.2022.9948977
M3 - Conference Proceeding
AN - SCOPUS:85143622319
T3 - 2022 IEEE 24th International Workshop on Multimedia Signal Processing, MMSP 2022
BT - 2022 IEEE 24th International Workshop on Multimedia Signal Processing, MMSP 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 24th IEEE International Workshop on Multimedia Signal Processing, MMSP 2022
Y2 - 26 September 2022 through 28 September 2022
ER -