Multi-Object Tracking

174 papers with code • 17 benchmarks • 30 datasets

Multi-Object Tracking is a task in computer vision that involves detecting and tracking multiple objects within a video sequence. The goal is to identify and locate objects of interest in each frame and then associate them across frames to keep track of their movements over time. This task is challenging due to factors such as occlusion, motion blur, and changes in object appearance, and is typically solved using algorithms that integrate object detection and data association techniques.


Use these libraries to find Multi-Object Tracking models and implementations

Most implemented papers

Simple Online and Realtime Tracking

abewley/sort 2 Feb 2016

This paper explores a pragmatic approach to multiple object tracking where the main focus is to associate objects efficiently for online and realtime applications.

FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking

ifzhang/FairMOT 4 Apr 2020

Formulating MOT as multi-task learning of object detection and re-ID in a single network is appealing since it allows joint optimization of the two tasks and enjoys high computation efficiency.

Towards Real-Time Multi-Object Tracking

Zhongdao/Towards-Realtime-MOT ECCV 2020

In this paper, we propose an MOT system that allows target detection and appearance embedding to be learned in a shared model.

Tracking without bells and whistles

phil-bergmann/tracking_wo_bnw ICCV 2019

Therefore, we motivate our approach as a new tracking paradigm and point out promising future research directions.

StrongSORT: Make DeepSORT Great Again

open-mmlab/mmtracking 28 Feb 2022

As a result, the construction of a good baseline for a fair comparison is essential.

ByteTrack: Multi-Object Tracking by Associating Every Detection Box

ifzhang/ByteTrack arXiv 2021

ByteTrack also achieves state-of-the-art performance on MOT20, HiEve and BDD100K tracking benchmarks.

MOT16: A Benchmark for Multi-Object Tracking

PaddlePaddle/PaddleDetection 2 Mar 2016

Recently, a new benchmark for Multiple Object Tracking, MOTChallenge, was launched with the goal of collecting existing and new data and creating a framework for the standardized evaluation of multiple object tracking methods.

Tracking Objects as Points

xingyizhou/CenterTrack ECCV 2020

Nowadays, tracking is dominated by pipelines that perform object detection followed by temporal association, also known as tracking-by-detection.

Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking

noahcao/OC_SORT CVPR 2023

Instead of relying only on the linear state estimate (i. e., estimation-centric approach), we use object observations (i. e., the measurements by object detector) to compute a virtual trajectory over the occlusion period to fix the error accumulation of filter parameters during the occlusion period.

PP-YOLOE: An evolved version of YOLO

PaddlePaddle/PaddleDetection 30 Mar 2022

In this report, we present PP-YOLOE, an industrial state-of-the-art object detector with high performance and friendly deployment.