Head Tracking 21

CroHD provides tracking annotation of pedestrian heads in densely populated video sequences. It consists of 2,276,838 human heads in 11,463 frames across 9 sequences of Full-HD resolution. We built CroHD upon 5 sequences from the publicly available MOTChallenge CVPR19 benchmark to enable performance comparison of trackers in the same scene between two paradigms - head tracking and pedestrian tracking. We further annotated 4 new sequences of higher crowd densities in two new scenarios. The new scenario centers on the Shibuya Train station and Shibuya Crossing, one of the busiest pedestrian crossings in the world. All sequences in CroHD have a framerate of 25fps and are captured from an elevated viewpoint. The sequences involve crowded indoor and outdoor scenes, recorded across different lighting and environmental conditions.

Training Set

Sample Name FPS Resolution Length Tracks BoxesDensityDescriptionSourceRef.
HT21-04251920x1080997 (00:40)580175479176.0Crowded outdoor train station.link[1]
HT21-03251920x10801000 (00:40)811257939257.9Crowded pedestrian crossing.link[1]
HT21-02251920x10803315 (02:13)1276733622221.3People leaving entrance of stadium by night time, elevated viewpoint.link[1]
HT21-01251920x1080429 (00:17)852145650.0Crowded indoor train station.link[1]
Total 5741 frm.
(230 s.)
2752 1188496 207.0

Test Set

Sample Name FPS Resolution Length Tracks BoxesDensityDescriptionSourceRef.
HT21-15251920x7341008 (00:40)321149821148.6A pedestrian street scene.link[1]
HT21-14251920x10801050 (00:42)1040258227245.9Crowded outdoor train station.link[1]
HT21-13251920x10801000 (00:40)734259603259.6Crowded pedestrian crossing.link[1]
HT21-12251920x10802080 (01:23)737380647183.0People leaving entrance of stadium by night time, elevated viewpoint.link[1]
HT21-11251920x1080585 (00:23)1333849265.8Crowded indoor train station.link[1]
Total 5723 frm.
(228 s.)
2965 1086790 189.9


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References:


[1] Sundararaman, R., Braga, C.D.A., Marchand, E. & Pettre, J. Tracking Pedestrian Heads in Dense Crowd. In Conference on Computer Vision and Pattern Recognition (CVPR), 2021.