Mean-Shift Tracking for Surveillance Applications Using Thermal and Visible Band Data Fusion

Date

2011-04-28

Author

Beyan, Cigdem
Temizel, Alptekin

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

205
views

0
downloads

Separate tracking of objects such as people and the luggages they carry is important for video surveillance applications as it would allow making higher level inferences and timely detection of potential threats. However, this is a challenging problem and in the literature, people and objects they carry are tracked as a single object. In this study, we propose using thermal imagery in addition to the visible band imagery for tracking in indoor applications (such as airports, metro or railway stations). We use adaptive background modeling in association with mean-shift tracking for fully automatic tracking. Trackers are refreshed using the background model to handle occlusion and split and to detect newly emerging objects as well as objects that leave the scene. Visible and thermal domain tracking information are fused to allow tracking of people and the objects they carry separately using their heat signatures. By using the trajectories of these objects, interactions between them could be deduced and potential threats such as abandoning of an object by a person could be detected in real-time. Better tracking performance is also achieved compared to using a single modality as thermal reflection and halo effect which adversely affect tracking are eliminated by the complementing visible band data. The proposed method has been tested on videos containing various scenarios. The experimental results show that the presented method is effective for separate tracking of objects such as people and their belongings and for detecting the interactions in the presence of occlusions.

Subject Keywords

Mean-shift, Visible image, Thermal image, Video surveillance, Multiple object tracking, Separate tracking

URI

https://hdl.handle.net/11511/32358

DOI

https://doi.org/10.1117/12.882838

Collections

Graduate School of Informatics, Conference / Seminar

Suggestions

OpenMETU
Core

Object tracking for surveillance applications using thermal and visible band video data fusion Beyan, Çiğdem; Temizel, Alptekin; Department of Information Systems (2010) Individual tracking of objects in the video such as people and the luggages they carry is important for surveillance applications as it would enable deduction of higher level information and timely detection of potential threats. However, this is a challenging problem and many studies in the literature track people and the belongings as a single object. In this thesis, we propose using thermal band video data in addition to the visible band video data for tracking people and their belongings separately for ...
A multimodal approach for individual tracking of people and their belongings Beyan, Çiğdem; Temizel, Alptekin (2015-04-01) In this study, a fully automatic surveillance system for indoor environments which is capable of tracking multiple objects using both visible and thermal band images is proposed. These two modalities are fused to track people and the objects they carry separately using their heat signatures and the owners of the belongings are determined. Fusion of complementary information from different modalities (for example, thermal images are not affected by shadows and there is no thermal reflection or halo effect in...
A continuous object tracking system with stationary and moving camera modes Emeksiz, Deniz; Temizel, Alptekin (2012-09-27) Automatic detection and tracking of objects get more important with the increasing number of surveillance cameras and mobile platforms having cameras. Tracking systems are either designed with stationary camera or designed to work in moving camera. When the camera is stationary, correspondence based tracking with background subtraction has a number of benefits such as enabling automatic detection of new objects in the scene and better tracking accuracy. On the other hand, mean shift is a histogram-based tra...
Evaluation of deep learning based multiple object trackers Moured, Omar; Akar, Gözde; Department of Electrical and Electronics Engineering (2020-9) Multiple object tracking (MOT) is a significant problem in the computer vision community due to its applications, including but not limited to, surveillance and emerging autonomous vehicles. The difficulties of this problem lie in several challenges, such as frequent occlusion, interaction, intra-class variations, in-and-out objects, etc. Recently, deep learning MOT methods confront these challenges effectively. State-of-the-art deep learning (DL) trackers pipeline consists of two stages, i.e., appearance h...
Object tracking system with seamless object handover between stationary and moving camera modes Emeksiz, Deniz; Temizel, Alptekin; Department of Information Systems (2012) As the number of surveillance cameras and mobile platforms with cameras increases, automated detection and tracking of objects on these systems gain importance. There are various tracking methods designed for stationary or moving cameras. For stationary cameras, correspondence based tracking methods along with background subtraction have various advantages such as enabling detection of object entry and exit in a scene. They also provide robust tracking when the camera is static. However, they fail when the ...

Citation Formats

C. Beyan and A. Temizel, “Mean-Shift Tracking for Surveillance Applications Using Thermal and Visible Band Data Fusion,” 2011, vol. 8020, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/32358.