4.7 Article

Enhancing the association in multi-object tracking via neighbor graph

Related references

Note: Only part of the references are listed.
Article Computer Science, Artificial Intelligence

MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking

Patrick Dendorfer et al.

Summary: Standardized benchmarks are crucial for pushing the performance of computer vision algorithms, with leaderboards offering objective measures of performance. MOTChallenge is a benchmark for single-camera Multiple Object Tracking launched in late 2014, focusing on multiple people tracking and aiming to create a framework for standardized evaluation of tracking methods.

INTERNATIONAL JOURNAL OF COMPUTER VISION (2021)

Proceedings Paper Automation & Control Systems

Joint Object Detection and Multi-Object Tracking with Graph Neural Networks

Yongxin Wang et al.

Summary: Object detection and data association are critical components in multi-object tracking systems. Recent works have shown that simultaneously optimizing detection and data association modules under a joint MOT framework can lead to improved performance. This study proposes a new instance of joint MOT approach based on Graph Neural Networks, which can model relations between variable-sized objects in both spatial and temporal domains, leading to state-of-the-art performance for both detection and MOT tasks.

2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2021) (2021)

Article Computer Science, Artificial Intelligence

Trust-aware generative adversarial network with recurrent neural network for recommender systems

Honglong Chen et al.

Summary: The paper introduces a Trust-Aware GAN with RNN for recommender systems, named TagRec, which utilizes user trust information to enhance recommendation accuracy. The discriminative and generative models collaborate in adversarial training to improve recommendation performance by generating samples that fit the user trust information.

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS (2021)

Article Computer Science, Artificial Intelligence

A benchmark for clothes variation in person re-identification

Kai Wang et al.

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS (2020)

Article Computer Science, Information Systems

Crowd Detection in Aerial Images Using Spatial Graphs and Fully-Convolutional Neural Networks

Giovanna Castellano et al.

IEEE ACCESS (2020)

Proceedings Paper Computer Science, Artificial Intelligence

Graph Convolutional Tracking

Junyu Gao et al.

2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019) (2019)

Proceedings Paper Computer Science, Software Engineering

Deep Association: End-to-end Graph-Based Learning for Multiple Object Tracking with Conv-Graph Neural Network

Cong Ma et al.

ICMR'19: PROCEEDINGS OF THE 2019 ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA RETRIEVAL (2019)

Article Engineering, Electrical & Electronic

Tracking Social Groups Within and Across Cameras

Francesco Solera et al.

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY (2017)

Proceedings Paper Computer Science, Artificial Intelligence

Tracking The Untrackable: Learning to Track Multiple Cues with Long-Term Dependencies

Amir Sadeghian et al.

2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV) (2017)

Proceedings Paper Computer Science, Artificial Intelligence

CityPersons: A Diverse Dataset for Pedestrian Detection

Shanshan Zhang et al.

30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) (2017)

Article Computer Science, Artificial Intelligence

Tracking of Multiple Target Types with a Single Neural Extended Kalman Filter

Kathleen A. Kramer et al.

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS (2010)

Article Engineering, Electrical & Electronic

Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics

Keni Bernardin et al.

EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING (2008)