Unsupervised Multiple-Object Tracking with a Dynamical Variational Autoencoder

This is the public dataset of synthetic trajectories and MOT17-3T, which is used in paper Unsupervised Multiple-Object Tracking with a Dynamical Variational Autoencoder. The source code can be found here.

cs.LG cs.CV Gaussian Processes and Bayesian Inference Generative Adversarial Networks and Image Synthesis Time Series Analysis and Forecasting Computer Science

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References (55)

01Probabilistic Tracklet Scoring and Inpainting for Multiple Object TrackingF. Saleh, Sadegh Aliakbarian, Hamid Rezatofighi et al.2020 · Computer Vision and Pattern Recognition · 86 citations In Library

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06Tracking Objects as PointsXingyi Zhou, V. Koltun, Philipp Krähenbühl2020 · European Conference on Computer Vision · 1.4k citations In Library

07UnOVOST: Unsupervised Offline Video Object Segmentation and TrackingJonathon Luiten, Idil Esen Zulfikar, B. Leibe2020 · IEEE Workshop/Winter Conference on Applications of Computer Vision · 65 citations In Library

08Exploiting Spatial Invariance for Scalable Unsupervised Object TrackingEric Crawford, Joelle Pineau2019 · AAAI Conference on Artificial Intelligence · 69 citations In Library

09A Recurrent Variational Autoencoder for Speech EnhancementSimon Leglaive, Xavier Alameda-Pineda, Laurent Girin et al.2019 · IEEE International Conference on Acoustics, Speech, and Signal Processing · 93 citations In Library

10Deep learning in video multi-object tracking: A surveyG. Ciaparrone, Francisco Luque Sánchez, S. Tabik et al.2019 · Neurocomputing · 670 citations In Library

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12Online Multi-Object Tracking Based on Feature Representation and Bayesian Filtering Within a Deep Learning ArchitectureJun Xiang, Guoshuai Zhang, Jianhua Hou2019 · IEEE Access · 31 citations

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Patents near this paper

Multi-Object Tracking using Online Metric Learning with Long Short-Term MemoryUS 10,957,053 · DEEPNORTH INC. · 2021 OBJECT TRACKING BY UNSUPERVISED LEARNINGUS 10,449,956 · FORD GLOBAL TECHNOLOGIES, LLC · 2019 AUTOMATED AND UNSUPERVISED GENERATION OF REAL-WORLD TRAINING DATAUS 10,755,144 · SENTIENT TECHNOLOGIES (BARBADOS) LIMITED · 2020 AUTOMATED AND UNSUPERVISED GENERATION OF REAL-WORLD TRAINING DATAUS 10,755,142 · SENTIENT TECHNOLOGIES (BARBADOS) LIMITED · 2020 DYNAMIC MULTI-CAMERA TRACKING OF MOVING OBJECTS IN MOTION STREAMSUS 11,069,072 · TATA CONSULTANCY SERVICES LIMITED · 2021

Source of record

arXiv