Deep MDP: A Modular Framework for Multi-Object Tracking
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Abstract (arXiv)
This paper presents a fast and modular framework for Multi-Object Tracking (MOT) based on the Markov descision process (MDP) tracking-by-detection paradigm. It is designed to allow its various functional components to be replaced by custom-designed alternatives to suit a given application. An interactive GUI with integrated object detection, segmentation, MOT and semi-automated labeling is also provided to help make it easier to get started with this framework. Though not breaking new ground in terms of performance, Deep MDP has a large code-base that should be useful for the community to try out new ideas or simply to have an easy-to-use and easy-to-adapt system for any MOT application. Deep MDP is available at https://github.com/abhineet123/deep_mdp.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 35 of its 60 references that are in the map. First posted on arXiv 22 Oct 2023.
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35 of 60 references.
References · 35
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
- 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
- 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,773
- 2018YOLOv3: An Incremental Improvementcited by 2,652
- 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,540
- 2016Performance Measures and a Data Set for Multi-Target, Multi-Camera Trackingcited by 685
- 2016Training Region-based Object Detectors with Online Hard Example Miningcited by 577
- 2016Fully-Convolutional Siamese Networks for Object Trackingcited by 566
- 2020HOTA: A Higher Order Metric for Evaluating Multi-Object Trackingcited by 546
- 2016MOT16: A Benchmark for Multi-Object Trackingcited by 531
23 more
- 2018SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networkscited by 445
- 2018Deep Learning for Generic Object Detection: A Surveycited by 392
- 2017Soft-NMS -- Improving Object Detection With One Line of Codecited by 380
- 2019Learning Discriminative Model Prediction for Trackingcited by 344
- 2018ATOM: Accurate Tracking by Overlap Maximizationcited by 285
- 2016ECO: Efficient Convolution Operators for Trackingcited by 239
- 2020Probabilistic Regression for Visual Trackingcited by 231
- 2018Distractor-aware Siamese Networks for Visual Object Trackingcited by 187
- 2018Acquisition of Localization Confidence for Accurate Object Detectioncited by 174
- 2019Deeper and Wider Siamese Networks for Real-Time Visual Trackingcited by 156
- 2015MOTChallenge 2015: Towards a Benchmark for Multi-Target Trackingcited by 133
- 2015UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Trackingcited by 98
- 2018Deep Affinity Network for Multiple Object Trackingcited by 82
- 2019Online Multi-Object Tracking with Dual Matching Attention Networkscited by 77
- 2017Tracking The Untrackable: Learning To Track Multiple Cues with Long-Term Dependenciescited by 76
- 2019How To Train Your Deep Multi-Object Trackercited by 71
- 2019FAMNet: Joint Learning of Feature, Affinity and Multi-dimensional Assignment for Online Multiple Object Trackingcited by 65
- 2019Deep Learning for Visual Tracking: A Comprehensive Surveycited by 60
- 2017Online Multi-Object Tracking Using CNN-based Single Object Tracker with Spatial-Temporal Attention Mechanismcited by 48
- 2014Deformable Part Models are Convolutional Neural Networkscited by 35
- 2017Tracking the Trackers: An Analysis of the State of the Art in Multiple Object Trackingcited by 18
- 2016A metric for sets of trajectories that is practical and mathematically consistentcited by 7
- 2019SiamVGG: Visual Tracking using Deeper Siamese Networkscited by 6
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