Object Tracking
584 papers with code • 7 benchmarks • 61 datasets
Object tracking is the task of taking an initial set of object detections, creating a unique ID for each of the initial detections, and then tracking each of the objects as they move around frames in a video, maintaining the ID assignment. State-of-the-art methods involve fusing data from RGB and event-based cameras to produce more reliable object tracking. CNN-based models using only RGB images as input are also effective. The most popular benchmark is OTB. There are several evaluation metrics specific to object tracking, including HOTA, MOTA, IDF1, and Track-mAP.
( Image credit: Towards-Realtime-MOT )
Benchmarks
These leaderboards are used to track progress in Object Tracking
Libraries
Use these libraries to find Object Tracking models and implementationsDatasets
KITTI
LaSOT
GOT-10k
MOTChallenge
VOT2018
Virtual KITTI
UAVDT
VOT2017
UA-DETRAC
Event-Camera Dataset
Subtasks
Most implemented papers
Lucid Data Dreaming for Video Object Segmentation
Our approach is suitable for both single and multiple object segmentation.
Segmenting Unknown 3D Objects from Real Depth Images using Mask R-CNN Trained on Synthetic Data
We train a variant of Mask R-CNN with domain randomization on the generated dataset to perform category-agnostic instance segmentation without any hand-labeled data and we evaluate the trained network, which we refer to as Synthetic Depth (SD) Mask R-CNN, on a set of real, high-resolution depth images of challenging, densely-cluttered bins containing objects with highly-varied geometry.
SiamVGG: Visual Tracking using Deeper Siamese Networks
It combines a Convolutional Neural Network (CNN) backbone and a cross-correlation operator, and takes advantage of the features from exemplary images for more accurate object tracking.
LiDARTag: A Real-Time Fiducial Tag System for Point Clouds
Because of the LiDAR sensors' nature, rapidly changing ambient lighting will not affect the detection of a LiDARTag; hence, the proposed fiducial marker can operate in a completely dark environment.
Probabilistic 3D Multi-Object Tracking for Autonomous Driving
Our method estimates the object states by adopting a Kalman Filter.
Rethinking the competition between detection and ReID in Multi-Object Tracking
JudasDie/SOTS
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However, the inherent differences and relations between detection and re-identification (ReID) are unconsciously overlooked because of treating them as two isolated tasks in the one-shot tracking paradigm.
HarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPS
The decoder part is inspired by the Cascaded Partial Decoder, known for fast and accurate salient object detection.
Model-free Vehicle Tracking and State Estimation in Point Cloud Sequences
The code and protocols for our benchmark and algorithm are available at https://github. com/TuSimple/LiDAR_SOT/.
Improving Object Detection, Multi-object Tracking, and Re-Identification for Disaster Response Drones
In the second approach, although DeepSORT only processes a quarter of all frames due to hardware and time limitations, our model with DeepSORT (42. 9%) outperforms FairMOT (71. 4%) in terms of recall.
MOTRv2: Bootstrapping End-to-End Multi-Object Tracking by Pretrained Object Detectors
DanceTrack/DanceTrack
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CVPR 2023
In this paper, we propose MOTRv2, a simple yet effective pipeline to bootstrap end-to-end multi-object tracking with a pretrained object detector.
