AP_HWDPL_p: Appearance Model with R-CNN


Benchmark:

Short name:

AP_HWDPL_p

Detector:

Public

Description:

Online tracking with appearance model based on R-CNN from Huawei (HWDPL).

Project page / code:

n/a

Reference:

C. Long, A. Haizhou, S. Chong, Z. Zijie, B. Bo. Online Multi-Object Tracking with Convolutional Neural Networks. In 2017 IEEE International Conference on Image Processing (ICIP), 2017.

Processing:

Online

Last submitted:

December 13, 2016 (3 years ago)

Published:

September 26, 2017 at 09:20:18 CET

Submissions:

3

Open source:

No

Hardware:

GTX 1080, 2.3 GHz, 1 Core

Benchmark performance:

Sequence MOTA IDF1 MOTP MT ML FP FN ID Sw.
2D MOT 201538.547.172.663.0270.04,00533,203586

Detailed performance:

Sequence MOTA IDF1 MOTP MT ML FP FN ID Sw.
ADL-Rundle-135.343.672.24.010.05735,41339
ADL-Rundle-343.351.977.67.09.06435,07647
AVG-TownCentre28.444.766.99.063.09414,005169
ETH-Crossing34.242.379.51.016.02063010
ETH-Jelmoli52.965.575.910.013.01781,00116
ETH-Linthescher39.447.575.217.0124.01855,18544
KITTI-1640.761.168.93.02.016083118
KITTI-1934.350.667.72.020.06632,80543
PETS09-S2L238.934.370.81.04.05525,164179
TUD-Crossing61.364.173.15.02.01440112
Venice-139.148.771.84.07.0762,6929

Raw data:


AP_HWDPL_p