Method

Dual way Aggregate View Object Detection architecture [la] [gp] [Bi-AVOD]


Submitted on 13 Aug. 2019 13:07 by
xusen guo (sun yet-sen university)

Running time:0.01 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
A dual-way network for 3D streaming-based object
detection and multi-object tracking is set up,
and a correlation module is introduced to compute
convolutional cross-correlation of adjacent
frames for temporal feature representation. The
multi-object tracking is done by performing a
imporved IoU tracker algorithm.
Parameters:
simga_l = 0.1
sigma_h = 0.5
t_min = 3
Latex Bibtex:

Detailed Results

From all 29 test sequences, our benchmark computes the commonly used tracking metrics CLEARMOT, MT/PT/ML, identity switches, and fragmentations [1,2]. The tables below show all of these metrics.


Benchmark MOTA MOTP MODA MODP
CAR 72.10 % 81.84 % 73.06 % 85.90 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 79.55 % 94.59 % 86.42 % 29485 1686 7578 15.16 % 34618 1156

Benchmark MT PT ML IDS FRAG
CAR 58.15 % 29.54 % 12.31 % 333 756

This table as LaTeX


[1] K. Bernardin, R. Stiefelhagen: Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. JIVP 2008.
[2] Y. Li, C. Huang, R. Nevatia: Learning to associate: HybridBoosted multi-target tracker for crowded scene. CVPR 2009.


eXTReMe Tracker