Method

Decoupled DeepSORT [Decoupled DeepSORT]


Submitted on 31 May. 2022 19:38 by
ahmed ramzi houalef (USTO-MB )

Running time:0.06 s
Environment:GPU @ 1.5 Ghz (Python)

Method Description:
This method is based on deepSORT, with many
optimisations. The major contribution is using a
decoupled tracker where each track is initialised
according to its class. Each class uses a
different feature extractor.
Parameters:
lambda = 0.5 for cars and 0 for pedestrian
nn_budget= 100 for cars and 10 for pedestrian
Latex Bibtex:

Detailed Results

From all 29 test sequences, our benchmark computes the HOTA tracking metrics (HOTA, DetA, AssA, DetRe, DetPr, AssRe, AssPr, LocA) [1] as well as the CLEARMOT, MT/PT/ML, identity switches, and fragmentation [2,3] metrics. The tables below show all of these metrics.


Benchmark HOTA DetA AssA DetRe DetPr AssRe AssPr LocA
CAR 45.75 % 48.46 % 43.77 % 51.97 % 73.58 % 50.52 % 65.79 % 79.06 %
PEDESTRIAN 32.02 % 34.10 % 30.43 % 39.37 % 56.88 % 38.34 % 46.56 % 73.36 %

Benchmark TP FP FN
CAR 22805 11587 1487
PEDESTRIAN 12221 10929 3802

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 59.09 % 75.36 % 61.98 % 996 42.75 %
PEDESTRIAN 34.12 % 68.72 % 36.37 % 520 17.61 %

Benchmark MT rate PT rate ML rate FRAG
CAR 37.69 % 48.62 % 13.69 % 1228
PEDESTRIAN 14.78 % 58.76 % 26.46 % 1507

Benchmark # Dets # Tracks
CAR 24292 685
PEDESTRIAN 16023 289

This table as LaTeX