Method

Fast DeepSort [FDS]


Submitted on 30 Jul. 2025 11:16 by
DJELLOUL MAZOUZ LAKHDAR (University of Sciences and Technology of Oran - MB)

Running time:0.01 s
Environment:GPU @ 2.0 Ghz (Python)

Method Description:
In this work, we propose a contribution to
detection and tracking using the DeepSort
algorithm. This contribution consists of making
modifications to the tracking part of this
algorithm. Our detector is based on YOLOv8m (You
Only Look Once Version 8 medium) trained on the
KITTI dataset (Karlsruhe Institute of Technology
and Toyota Technological Institute), and tracking
is based on DeepSort but with the use of a Faster-
Filter for tracking.
Parameters:
max_cosine_distance = 0.5
nn_budget = 100 features to cache
max_iou_distance = 0.7
max_age = 60
n_init = 3
Latex Bibtex:
@misc{djelltrea2025,
title={Fast DeepSort},
author={Djelloul Mazouz Lakhdar, Kaddour Trea
Abdessamad, Amiour Tarek},
year={2025},
note={Unpublished}

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 79.17 % 83.93 % 80.39 % 87.26 %
PEDESTRIAN 48.51 % 74.62 % 50.64 % 93.34 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 83.40 % 98.65 % 90.39 % 31702 435 6308 3.91 % 35195 1247
PEDESTRIAN 57.56 % 89.86 % 70.17 % 13442 1517 9909 13.64 % 17405 477

Benchmark MT PT ML IDS FRAG
CAR 56.92 % 34.00 % 9.08 % 422 880
PEDESTRIAN 25.43 % 41.24 % 33.33 % 494 1219

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.


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