This tracker operates in three stages: First, objects are detected in each frame independently using the DPM object detector by Ross Girshick and Pedro Felzenszwalb. Second, all detections with a positive score are associated to detections in the next frame using appearance and the bounding box overlap. We predict objects to the next frame using a Kalman filter and associate them globally via the Hungarian method for bipartite matching. To gap occlusions and missed detections, we also associate tracklets with each other in a first stage. Similarly to the second stage the Hungarian algorithm is employed but this time based on a occlusion sensitive appearance model and regression of the bounding boxes in one tracklet from the bounding boxes in the other tracklet. The algorithm outputs all associated tracklets with a lifetime longer than three frames.
The reported running time is dominated by the object detection stage.
Project page / code:
A. Geiger, M. Lauer, C. Wojek, C. Stiller, R. Urtasun. 3D Traffic Scene Understanding from Movable Platforms. In Pattern Analysis and Machine Intelligence (PAMI), 2014.
March 30, 2016 (4 years ago)
March 31, 2016 at 07:34:42 CET
3 GHz, 1 CPU
|MOT16||33.7||0.0||76.5||55 (7.2)||411 (54.2)||5,804||112,587||38.2||92.3||1.0||2,418 (63.2)||2,252 (58.9)|