Effective collision risk reduction in autonomous vehicles relies on robust and straightforward pedestrian tracking. Challenges posed by occlusion and switching scenarios significantly impede the reliability of pedestrian tracking. In the current stud...
Daily behavioral analysis of group-housed pigs provides critical insights into early warning systems for pig health issues and animal welfare in smart pig farming. In this study, our main objective was to develop an automated method for monitoring an...
Artificial reefs (ARS) are structures placed in the ocean, and their posture and position affect the placement effect of the reef. Quantitative data on the reef posture and position can provide references for the creation of a favorable environment f...
As an essential part of intelligent monitoring, behavior recognition, automatic driving, and others, the challenge of multi-object tracking is still to ensure tracking accuracy and robustness, especially in complex occlusion environments. Aiming at t...
Multi-Object Tracking (MOT) is an integral part of any autonomous driving pipelines because it produces trajectories of other moving objects in the scene and predicts their future motion. Thanks to the recent advances in 3D object detection enabled b...
Due to the reasonably acceptable performance of state-of-the-art object detectors, tracking-by-detection is a standard strategy for visual multi-object tracking (MOT). In particular, online MOT is more demanding due to its diverse applications in tim...
Achieving robust multi-object tracking in complex real-world scenarios remains a challenging task. Existing approaches often struggle to effectively handle occlusion, primarily because occlusion can result in unreliable appearance features, inaccurat...
In remote sensing images, small target size and diverse background cause difficulty in locating targets accurately and quickly. To address the lack of accuracy and inefficient real-time performance of existing tracking algorithms, a multi-object trac...
Multi-object tracking (MOT) aims to detect objects in video sequences and associate them across frames. Currently, the mainstream research direction regarding MOT is the tracking-by-detection (TBD) framework. Tracking results are highly sensitive to...
Currently, intelligent security systems are widely deployed in indoor buildings to ensure the safety of people in shopping malls, banks, train stations, and other indoor buildings. Multi-Object Tracking (MOT), as an important component of intelligent...
Multi-object tracking (MOT) is a significant and widespread research field in image processing and computer vision. The goal of the MOT task consists in predicting the complete tracklets of multiple objects in a video sequence. There are usually many...
With the current demand for automation in the agro-food industry, accurately detecting and localizing relevant objects in 3D is essential for successful robotic operations. However, this is a challenge due the presence of occlusions. Multi-view perce...
Multi-object tracking (MOT) is an important research topic in the field of computer vision, including object detection and data association. However, problems such as missed detection and trajectory mismatch often lead to missing target information,...
The long-term behavioural analysis and health assessment of Pigs are essential for intelligent management in modern pig farming. Manual tracking and behaviour analysis for constructing health assessment systems are often subjective, inconsistent, and...
Multi-object tracking (MOT) is a prominent and important study in point cloud processing and computer vision. The main objective of MOT is to predict full tracklets of several objects in point cloud. Occlusion and similar objects are two common probl...
Multi-object pedestrian tracking plays a crucial role in autonomous driving systems, enabling accurate perception of the surrounding environment. In this paper, we propose a comprehensive approach for pedestrian tracking, combining the improved YOLOv...
The current popular one-shot multi-object tracking (MOT) algorithms are dominated by the joint detection and embedding paradigm, which have high inference speeds and accuracy, but their tracking performance is unstable in crowded scenes. Not only doe...
Multi-object tracking still faces significant challenges in complex conditions such as dense scenes, occlusion environments, and non-linear motion, especially regarding the detection and identity maintenance of small objects. To tackle these issues,...
Multi-Object Tracking (MOT) remains a fundamental challenge in computer vision, with significant applications in autonomous systems, surveillance, and intelligent transportation. The Tracking-By-Detection (TBD) paradigm has achieved notable tracking...
Pedestrian tracking is an important aspect of autonomous vehicles environment perception in a vehicle running environment. The performance of the existing pedestrian tracking algorithms is limited by the complex traffic environment, the changeable ap...
Multi-object tracking (MOT) is a topic of great interest in the field of computer vision, which is essential in smart behavior-analysis systems for healthcare, such as human-flow monitoring, crime analysis, and behavior warnings. Most MOT methods ach...
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 application in navigation tasks of assistive mobile robots, with the aim to increase...
Multi-object tracking aims to track multiple objects across consecutive frames in a video, assigning a unique classifier to each object. However, issues such as occlusions, directional changes, or shape alterations can cause appearance variations, le...
This paper presents OcclusionTrack (OCCTrack), a robust multi-object tracker designed to address occlusion challenges in dense scenes. Occlusion remains a critical issue in multi-object tracking; despite significant advancements in current tracking m...
Multi-object tracking (MOT) is a key intermediate task in many practical applications and theoretical fields, facing significant challenges due to complex scenarios, particularly in the context of drone-based air-to-ground military operations. During...
Accurate multi-object tracking (MOT) is essential for autonomous vehicles, enabling them to perceive and interact with dynamic environments effectively. Single-modality 3D MOT algorithms often face limitations due to sensor constraints, resulting in...
This work presents an autonomous vision-based mobile target tracking and following system designed for unmanned aerial vehicles (UAVs) leveraging multi-target information. It explores the research gap in applying the most recent multi-object tracking...
Effective livestock management is critical for cattle farms in today’s competitive era of smart modern farming. To ensure farm management solutions are efficient, affordable, and scalable, the manual identification and detection of cattle are n...
Multi-object tracking stands as one of the most prominent domains in Computer Vision and has significant research value and practical importance. However, due to the complexity of scenarios in the real world, especially in crowded environments with f...
As a result of increasing urbanization, traffic monitoring in cities has become a challenging task. The use of Unmanned Aerial Vehicles (UAVs) provides an attractive solution to this problem. Multi-Object Tracking (MOT) for UAVs is a key technology t...
Multiple-object tracking (MOT) is a fundamental task in computer vision and is widely applied across various domains. However, its algorithms remain somewhat immature in practical applications. To address the challenges presented by complex scenarios...
The human brain can effortlessly perform vision processes using the visual system, which helps solve multi-object tracking (MOT) problems. However, few algorithms simulate human strategies for solving MOT. Therefore, devising a method that simulates...
Multi-object tracking (MOT) is a critical task in computer vision, with widespread applications in intelligent surveillance, behavior analysis, autonomous navigation, and marine ecological monitoring. In particular, accurate tracking of underwater fi...
During the Multi-Object Tracking (MOT) process, the effectiveness of Re-Identification (Re-ID) is typically contingent upon the performance of detection. In particular, detection errors may result in the blurring of Re-ID features, thereby diminishin...
Recognizing the identity of a query individual in a surveillance sequence is the core of Multi-Object Tracking (MOT) and Re-Identification (Re-Id) algorithms. Both tasks can be addressed by measuring the appearance affinity between people observation...
On-satellite information processing enables all-weather target tracking. The background of videos from satellite sensors exhibits an affine transformation due to their motion relative to the Earth. In complex moving backgrounds, moving vehicles have...
Multi-Object Tracking (MOT) technology is dedicated to continuously tracking multiple targets of interest in a sequence of images and accurately identifying their specific positions at different times. This technology is crucial in key application ar...
Multi-object tracking (MOT) algorithms are a key research direction in the field of computer vision. Among them, the joint detection and embedding (JDE) method, with its excellent speed and accuracy performance, has become the current mainstream solu...
Multi object tracking (MOT) is a key research technology in the environment sensing system of automatic driving, which is very important to driving safety. Online multi object tracking needs to accurately extend the trajectory of multiple objects wit...
The integration of semantic simultaneous localization and mapping (SLAM) with 3D object detection in indoor scenes is a significant challenge in the field of robot perception. Existing methods typically rely on expensive sensors and lack robustness a...
This paper explored a pragmatic approach to research the real-time performance of a multiway concurrent multiobject tracking (MOT) system. At present, most research has focused on the tracking of single-image sequences, but in practical applications,...
Multi-object tracking (MOT) is a key technology for enabling autonomous navigation of unmanned surface vehicle (USV) as it provides continuous perception of surrounding maritime targets and supports navigation decision-making. However, videos acquire...
Multi-object tracking (MOT) aims at estimating locations and identities of objects in videos. Many modern multiple-object tracking systems follow the tracking-by-detection paradigm, consisting of a detector followed by a method for associating detect...
Three-dimensional multi-object tracking (MOT) using lidar point cloud data is crucial for applications in autonomous driving, smart cities, and robotic navigation. It involves identifying objects in point cloud sequence data and consistently assignin...
Maritime ship target tracking plays an important role in applications such as maritime patrol and maritime surveillance. However, complex sea conditions, similar target appearances, and long-distance imaging often lead to target identity confusion an...
Falls are a major problem in hospitals, and physical or chemical restraints are commonly used to “protect” patients in hospitals and service users in hostels, especially elderly patients with dementia. However, physical and chemical restr...
Smart farming technologies to track and analyze pig behaviors in natural environments are critical for monitoring the health status and welfare of pigs. This study aimed to develop a robust multi-object tracking (MOT) approach named YOLOv8 + OC-SORT(...
Models based on joint detection and re-identification (ReID), which significantly increase the efficiency of online multi-object tracking (MOT) systems, are an evolution from separate detection and ReID models in the tracking-by-detection (TBD) parad...
Current multi-target multi-camera tracking algorithms demand increased requirements for re-identification accuracy and tracking reliability. This study proposed an improved end-to-end multi-target tracking algorithm that adapts to multi-view multi-sc...
In pig farming, multi-object tracking (MOT) algorithms are effective tools for identifying individual pigs and monitoring their health, which enhances management efficiency and intelligence. However, due to the considerable variation in breeding envi...
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