DETRAC-Train-Annotations-XML: contains full annotations with the attribute information (e.g., vehicle category, weather and scale), which is used for detection training.
DETRAC-Train-Annotations-MAT: contains position information of target trajectories out of the general background regions ignored in the benchmark, which is used for detection and tracking evaluation.
DETRAC-Train-Annotations-XML-v3: contains improved annotations with the attribute information (e.g., vehicle category and color, weather and scale), which is used for detection, tracking and counting training.
DETRAC-Sequence-Locations: contains the specific location information for each sequence (24 different locations).
DETRAC-Test-Annotations-XML: contains full annotations with the attribute information (e.g., vehicle category, weather and scale), which is used for detection training.
DETRAC-Test-Annotations-MAT: contains position information of target trajectories out of the general background regions ignored in the benchmark, which is used for detection and tracking evaluation.
Toolkit
DETRAC-toolkit-train (Windows beta): contains the actual evaluation toolkit and several state-of-the-art trackers(i.e., CEM, CMOT, DCT, FH2T, GOG, H2T, IHTILS and RMOT). The codes of the trackers are publicly available or provided by the authors.
DETRAC-toolkit-test-det (Windows beta): evaluation tool for the detection dataset.
DETRAC-toolkit-test-trk (Windows beta): evaluation tool for the tracking dataset.
Detection methods
DPM:
P. F. Felzenszwalb, R. B. Girshick, D. A. McAllester, and D. Ramanan.
Object detection with discriminatively trained part-based models.
In TPAMI, 32(9):1627-1645, 2010.
ACF:
P. Dollár, R. Appel, S. Belongie, and P. Perona.
Fast feature pyramids for object detection.
In TPAMI, 36(8):1532-1545, 2014.
R-CNN:
R. B. Girshick, J. Donahue, T. Darrell, and J. Malik.
Rich feature hierarchies for accurate object detection and semantic segmentation.
In CVPR, pages 580-587, 2014.
CompACT:
Z. Cai, M. Saberian, and N. Vasconcelos.
Learning complexity-aware cascades for deep pedestrian detection.
In ICCV, 2015.
Tracking methods
CEM: A. Andriyenko and K. Schindler. Multi-target tracking by continuous energy minimization. In CVPR, pages 1265-1272, 2011.
CMOT: S. H. Bae and K. Yoon. Robust online multi-object tracking based on tracklet confidence and online discriminative appearance learning. In CVPR, pages 1218-1225, 2014.
DCT: A. Andriyenko, K. Schindler, and S. Roth. Discrete-continuous optimization for multi-target tracking. In CVPR, pages 1926-1933, 2012.
H2T: L. Wen, W. Li, Z. Lei, D. Yi, and S. Z. Li. Multiple target tracking based on undirected hierarchical relation hypergraph. In CVPR, pages 3457-3464, 2014.
GOG: H. Pirsiavash, D. Ramanan, and C. C. Fowlkes. Globally-optimal greedy algorithms for tracking a variable number of objects. In CVPR, pages 1201-1208, 2011.
IHTLS: C. Dicle, O. I. Camps, and M. Sznaier. The way they move: Tracking multiple targets with similar appearance. In ICCV, pages 2304-2311, 2013.