Benchmark:

MOT16 |

Short name:

TBD_16

Detector:

Public

Description:

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.

Default parameters.

Reference:

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.

Last submitted:

March 30, 2016 (8 years ago)

Published:

March 31, 2016 at 07:34:42 CET

Submissions:

1

Project page / code:

http://www.cvlibs.net/software/trackbydet/

Open source:

Yes

Hardware:

3 GHz, 1 CPU

Runtime:

1.3 Hz

Benchmark performance:

Detailed performance:

Raw data:

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