7 research outputs found
Enhancing Multi-Camera People Tracking with Anchor-Guided Clustering and Spatio-Temporal Consistency ID Re-Assignment
Multi-camera multiple people tracking has become an increasingly important
area of research due to the growing demand for accurate and efficient indoor
people tracking systems, particularly in settings such as retail, healthcare
centers, and transit hubs. We proposed a novel multi-camera multiple people
tracking method that uses anchor-guided clustering for cross-camera
re-identification and spatio-temporal consistency for geometry-based
cross-camera ID reassigning. Our approach aims to improve the accuracy of
tracking by identifying key features that are unique to every individual and
utilizing the overlap of views between cameras to predict accurate trajectories
without needing the actual camera parameters. The method has demonstrated
robustness and effectiveness in handling both synthetic and real-world data.
The proposed method is evaluated on CVPR AI City Challenge 2023 dataset,
achieving IDF1 of 95.36% with the first-place ranking in the challenge. The
code is available at: https://github.com/ipl-uw/AIC23_Track1_UWIPL_ETRI
1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results
The 1 Workshop on Maritime Computer Vision (MaCVi) 2023 focused
on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned
Surface Vehicle (USV), and organized several subchallenges in this domain: (i)
UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking,
(iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime
Obstacle Detection. The subchallenges were based on the SeaDronesSee and MODS
benchmarks. This report summarizes the main findings of the individual
subchallenges and introduces a new benchmark, called SeaDronesSee Object
Detection v2, which extends the previous benchmark by including more classes
and footage. We provide statistical and qualitative analyses, and assess trends
in the best-performing methodologies of over 130 submissions. The methods are
summarized in the appendix. The datasets, evaluation code and the leaderboard
are publicly available at https://seadronessee.cs.uni-tuebingen.de/macvi.Comment: MaCVi 2023 was part of WACV 2023. This report (38 pages) discusses
the competition as part of MaCV
Method for Obtaining Better Traffic Survey Data
Road traffic surveys determine the number and type of vehicles passing by a specific point over a certain period of time. The manual estimation of the number and type of vehicles from images captured by a camera is the most commonly used method. However, this method has the disadvantage of requiring high amounts of manpower and cost. Recently, methods of automating traffic volume surveys using sensors or deep learning have been widely attempted, but there is the disadvantage that a person must finally manually verify the data in order to ensure that they are reliable. In order to address these shortcomings, we propose a method for efficiently conducting road traffic volume surveys and obtaining highly reliable data. The proposed method detects vehicles on the road from CCTV (Closed-circuit television) images and classifies vehicle types using deep learning or a similar method. After that, it automatically informs the user of candidates with a high probability of error and provides a method for efficient verification. The performance of the proposed method was tested using a data set collected by an actual road traffic survey company. As a result, we proved that our method shows better accuracy than the previous method. The proposed method can reduce the labor and cost in road traffic volume surveys, and increase the reliability of the data due to more accurate results
UA-DETRAC 2018: Report of AVSS2018 & IWT4S Challenge on Advanced Traffic Monitoring
A desirable smart traffic-monitoring and street-safety system can elicit and support the intervention of law enforcement agencies or medical staff. Recently, there has been a dramatically higher demand for such smart systems. To this end, the International Workshop on Traffic and Street Surveillance for Safety and Security (IWT4S) was organized in conjunction with the 15th IEEE International Conference on Advanced Video and Signal-based Surveillance (AVSS 2018). Our goal is to advance the state-of-the-art detection and tracking algorithms and provide a comprehensive performance evaluation for them. We evaluate 5 submitted detection and 7 submitted tracking methods on the large-scale UA-DETRAC benchmark, and the results are shared publicly on the website http://detrac-db. rit.albany.edu. We expect this challenge to advance the research and development of new detection and tracking methods for transportation applications