The KITTI Vision Benchmark Suite

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.

See implementation.

@ARTICLE{Geiger2014PAMI,
author = {Andreas Geiger and Martin Lauer and Christian Wojek and Christoph Stiller and Raquel Urtasun},
title = {3D Traffic Scene Understanding from Movable Platforms},
journal = {Pattern Analysis and Machine Intelligence (PAMI)},
year = {2014}
}
@INPROCEEDINGS{Zhang2013ICCV,
author = {Hongyi Zhang and Andreas Geiger and Raquel Urtasun},
title = {Understanding High-Level Semantics by Modeling Traffic Patterns},
booktitle = {International Conference on Computer Vision (ICCV)},
year = {2013}
}