TY - GEN
T1 - What do I see? Modeling human visual perception for multi-person tracking
AU - Yan, Xu
AU - Kakadiaris, Ioannis A.
AU - Shah, Shishir K.
N1 - Funding Information:
This work was supported in part by the US Department of Justice, grant number 2009-MU-MU-K004. Any opinions, findings, conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of our sponsors.
PY - 2014
Y1 - 2014
N2 - This paper presents a novel approach for multi-person tracking utilizing a model motivated by the human vision system. The model predicts human motion based on modeling of perceived information. An attention map is designed to mimic human reasoning that integrates both spatial and temporal information. The spatial component addresses human attention allocation to different areas in a scene and is represented using a retinal mapping based on the log-polar transformation while the temporal component denotes the human attention allocation to subjects with different motion velocity and is modeled as a static-dynamic attention map. With the static-dynamic attention map and retinal mapping, attention driven motion of the tracked target is estimated with a center-surround search mechanism. This perception based motion model is integrated into a data association tracking framework with appearance and motion features. The proposed algorithm tracks a large number of subjects in complex scenes and the evaluation on public datasets show promising improvements over state-of-the-art methods.
AB - This paper presents a novel approach for multi-person tracking utilizing a model motivated by the human vision system. The model predicts human motion based on modeling of perceived information. An attention map is designed to mimic human reasoning that integrates both spatial and temporal information. The spatial component addresses human attention allocation to different areas in a scene and is represented using a retinal mapping based on the log-polar transformation while the temporal component denotes the human attention allocation to subjects with different motion velocity and is modeled as a static-dynamic attention map. With the static-dynamic attention map and retinal mapping, attention driven motion of the tracked target is estimated with a center-surround search mechanism. This perception based motion model is integrated into a data association tracking framework with appearance and motion features. The proposed algorithm tracks a large number of subjects in complex scenes and the evaluation on public datasets show promising improvements over state-of-the-art methods.
UR - http://www.scopus.com/inward/record.url?scp=84906512970&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84906512970&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-10605-2_21
DO - 10.1007/978-3-319-10605-2_21
M3 - Conference contribution
AN - SCOPUS:84906512970
SN - 9783319106045
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 314
EP - 329
BT - Computer Vision, ECCV 2014 - 13th European Conference, Proceedings
PB - Springer-Verlag
T2 - 13th European Conference on Computer Vision, ECCV 2014
Y2 - 6 September 2014 through 12 September 2014
ER -