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note={Unpublished}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 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 65.61 % 66.87 % 64.99 % 69.94 % 84.34 % 67.50 % 85.75 % 85.58 %
PEDESTRIAN 40.58 % 40.06 % 41.42 % 43.75 % 68.89 % 45.87 % 66.48 % 78.04 %

Benchmark TP FP FN
CAR 28024 6368 495
PEDESTRIAN 13188 9962 1515

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 77.90 % 83.76 % 80.05 % 739 64.66 %
PEDESTRIAN 48.00 % 74.23 % 50.42 % 560 33.33 %

Benchmark MT rate PT rate ML rate FRAG
CAR 56.15 % 34.46 % 9.38 % 588
PEDESTRIAN 25.43 % 41.24 % 33.33 % 939

Benchmark # Dets # Tracks
CAR 28519 984
PEDESTRIAN 14703 421

This table as LaTeX

This figure as: png pdf

This figure as: png pdf

[1] J. Luiten, A. Os̆ep, P. Dendorfer, P. Torr, A. Geiger, L. Leal-Taixé, B. Leibe: HOTA: A Higher Order Metric for Evaluating Multi-object Tracking. IJCV 2020.
[2] K. Bernardin, R. Stiefelhagen: Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. JIVP 2008.
[3] Y. Li, C. Huang, R. Nevatia: Learning to associate: HybridBoosted multi-target tracker for crowded scene. CVPR 2009.


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