Developing object detection, tracking and image mosaicing algorithms for visual surveillance

Visual surveillance systems are becoming increasingly important in the last decades due to proliferation of cameras. These systems have been widely used in scientific, commercial and end-user applications where they can store, extract and infer huge amount of information automatically without human help. In this thesis, we focus on developing object detection, tracking and image mosaicing algorithms for a visual surveillance system. First, we review some real-time object detection algorithms that exploit motion cue and enhance one of them that is suitable for use in dynamic scenes. This algorithm adopts a nonparametric probabilistic model over the whole image and exploits pixel adjacencies to detect foreground regions under even small baseline motion. Then we develop a multiple object tracking algorithm which utilizes this algorithm as its detection step. The algorithm analyzes multiple object interactions in a probabilistic framework using virtual shells to track objects in case of severe occlusions. The final part of the thesis is devoted to an image mosaicing algorithm that stitches ordered images to create a large and visually attractive mosaic for large sequence of images. The proposed mosaicing method eliminates nonlinear optimization techniques with the capability of real-time operation on large datasets. Experimental results show that developed algorithms work quite successfully in dynamic and cluttered environments with real-time performance

A modular software architecture for UAVs

There have been several attempts to create scalable and hardware independent software architectures for Unmanned Aerial Vehicles (UAV). In this work, we propose an onboard architecture for UAVs where hardware abstraction, data storage and communication between modules are efficiently maintained. All processing and software development is done on the UAV while state and mission status of the UAV is monitored from a ground station. The architecture also allows rapid development of mission-specific third party applications on the vehicle with the help of the core module

Real-time multiple object tracking using virtual shells

This paper presents a new multiple object tracking framework where arbitrarily shaped complex objects having severe occlusions are successfully tracked in real-time using stationary cameras. The proposed method utilizes the concept of a virtual shell that encloses each moving object in the scene and simplifies analysis of object interactions. Object’s state transitions are handled by an event resolution analysis. Such a regional analysis boosts the tracking process. Finally, a pixel level evaluation is employed for all objects to establish successful pixel memberships. The proposed tracking algorithm is experimentally tested on some public databases and our own challenging aquarium setup that contains many different fish interactions

A new approach to real-time mosaicing of aerial images

We present a new image mosaicing technique that uses sequential aerial images captured from a camera and is capable of creating consistent large scale mosaics in real-time. To find the alignment of every new image, we use all the available images in the mosaic that have intersection with the new image instead of using only the previous one. To detect image intersections in an efficient manner, we utilize ‘Separating Axis Theorem’, a geometric tool from computer graphics which is used for collision detection. Moreover, after a certain number of images are added to the mosaic, a novel affine refinement procedure is carried out to increase global consistency. Finally, gain compensation and multi-band blending are optionally used as offline steps to compensate for photometric defects and seams caused by misregistrations. Proposed approach is tested on some public datasets and it is compared with two state-of-the-art algorithms. Results are promising and show the potential of our algorithm in various practical scenarios