A multimodal approach for individual tracking of people and their belongings

Beyan, Çiğdem
Temizel, Alptekin
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 visible images) is shown to result in better object detection performance. We use adaptive background modeling and local intensity operation for object detection and the mean-shift tracking algorithm for fully automatic tracking. Trackers are refreshed to resolve potential problems which may occur due to the changes in object's size, shape and to handle occlusion-split and to detect newly emerging objects as well as objects that leave the scene. The proposed scheme is applied to the abandoned object detection problem and the results are compared with the state of art methods. The results show that the proposed method facilitate individual tracking of objects for various applications, and provide lower false alarm rates compared to the state of art methods when applied to the abandoned object detection problem.


Mean-Shift Tracking for Surveillance Applications Using Thermal and Visible Band Data Fusion
Beyan, Cigdem; Temizel, Alptekin (2011-04-28)
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 u...
Visual detection and tracking of moving objects
Ergezer, Hamza; Leblebicioğlu, Mehmet Kemal (2007-06-13)
In this paper, primary steps of a visual surveillance system are presented: moving object detection and tracking of these moving objects. Running average method has been used to detect the moving objects in the video, which is taken from a static camera. Tracking of foreground objects has been realized by using a Kalman filter. After background subtraction, morphological operators are used to remove noises detected as foreground. Active contour models (snakes) are the segmentation tools for the extracted fo...
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...
A Computationally Efficient Appearance-Based Algorithm for Geospatial Object Detection
Arslan, Duygu; Alatan, Abdullah Aydın (2012-04-27)
A computationally efficient appearance-based algorithm for geospatial object detection is presented and evaluated specifically for aircraft detection from satellite imagery. An aircraft operator exploiting the edge information via gray level differences between the aircraft and its background is constructed with Haar-like polygon regions by using the shape information of the aircraft as an invariant. Fast evaluation of the aircraft operator is achieved by means of integral image. Rotated integral images are...
3D Extended Object Tracking Using Recursive Gaussian Processes
Kumru, Murat; Özkan, Emre (2018-07-10)
In this study, we consider the challenging task of tracking dynamic 3D objects with unknown shapes by using sparse point cloud measurements gathered from the surface of the objects. We propose a Gaussian process based algorithm that is capable of tracking the dynamic behavior of the object and learn its shape in 3D simultaneously. Our solution does not require any parametric model assumption for the unknown shape. The shape of the objects is learned online via a Gaussian process. The proposed method can joi...
Citation Formats
Ç. Beyan and A. Temizel, “A multimodal approach for individual tracking of people and their belongings,” IMAGING SCIENCE JOURNAL, pp. 192–202, 2015, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/32188.