Hota: A higher order metric for evaluating multi-object tracking
Multi-object tracking (MOT) has been notoriously difficult to evaluate. Previous metrics
overemphasize the importance of either detection or association. To address this, we …
overemphasize the importance of either detection or association. To address this, we …
Motchallenge: A benchmark for single-camera multiple target tracking
Standardized benchmarks have been crucial in pushing the performance of computer vision
algorithms, especially since the advent of deep learning. Although leaderboards should not …
algorithms, especially since the advent of deep learning. Although leaderboards should not …
Recent advances in embedding methods for multi-object tracking: a survey
Multi-object tracking (MOT) aims to associate target objects across video frames in order to
obtain entire moving trajectories. With the advancement of deep neural networks and the …
obtain entire moving trajectories. With the advancement of deep neural networks and the …
Online multi-object tracking with unsupervised re-identification learning and occlusion estimation
Occlusion between different objects is a typical challenge in Multi-Object Tracking (MOT),
which often leads to inferior tracking results due to the missing detected objects. The …
which often leads to inferior tracking results due to the missing detected objects. The …
Sort and deep-SORT based multi-object tracking for mobile robotics: Evaluation with new data association metrics
Multi-Object Tracking (MOT) techniques have been under continuous research and
increasingly applied in a diverse range of tasks. One area in particular concerns its …
increasingly applied in a diverse range of tasks. One area in particular concerns its …
Near-online tracking with co-occurrence constraints in blockchain-based edge computing
Multiobject tracking is a basic task in video analysis. Due to the strict requirements on
efficiency and resource consumption, most of the applications on edge devices are online or …
efficiency and resource consumption, most of the applications on edge devices are online or …
Object detection methods on compressed domain videos: An overview, comparative analysis, and new directions
D Zhai, X Zhang, X Li, X Xing, Y Zhou, C Ma - Measurement, 2023 - Elsevier
A review of the object detection on compressed domain video is presented in this paper. The
literature reviewed spans over the past 32 years (1990–2022), covering the key methods for …
literature reviewed spans over the past 32 years (1990–2022), covering the key methods for …
Non-semantics suppressed mask learning for unsupervised video semantic compression
Most video compression methods aim to improve the decoded video visual quality, instead
of particularly guaranteeing the semantic-completeness, which deteriorates downstream …
of particularly guaranteeing the semantic-completeness, which deteriorates downstream …
A coding framework and benchmark towards low-bitrate video understanding
Video compression is indispensable to most video analysis systems. Despite saving the
transportation bandwidth, it also deteriorates downstream video understanding tasks …
transportation bandwidth, it also deteriorates downstream video understanding tasks …
Occlusion-robust online multi-object visual tracking using a GM-PHD filter with CNN-based re-identification
NL Baisa - Journal of Visual Communication and Image …, 2021 - Elsevier
We propose a novel online multi-object visual tracker using a Gaussian mixture Probability
Hypothesis Density (GM-PHD) filter and deep appearance learning. The GM-PHD filter has …
Hypothesis Density (GM-PHD) filter and deep appearance learning. The GM-PHD filter has …