This work presents an approach to detect moving objects from Unmanned Aerial Vehicles (UAV). A common framework for most of the existing techniques is using image registration to warp consecutive frames as an ego-motion compensation step and applying frame differencing to detect the moving objects. Assuming a planar scene, we propose the exploitation of telemetry information available from Global Positioning and Inertial Navigation Systems (GPS/INS) to estimate a similarity transformation matrix that would map the image points from one frame to another. In this work, we show that the telemetry-based image registration combined with global registration methods produces more accurate results than the traditional image registration techniques in case of a scene with poor or no texture. To segment the moving objects, we employ the probabilistic background modelling method with mixture of Gaussian distributions.

Automatic baggage detection has become a subject of significant practical interest in recent years. In this paper, we propose an approach to baggage detection in CCTV video footage that uses color information to address some of the vital shortcomings of state-of-the-art algorithms. The proposed approach consists of typical steps used in baggage detection, namely, the estimation of moving direction of humans carrying baggage, construction of human-like temporal templates, and their alignment with the best matched view-specific exemplars. In addition, we utilize the color information to define the region that most likely belongs to a human torso in order to reduce the false positive detections. A key novel contribution is the person's viewing direction estimation using machine learning and shoulder shape related features. Further enhancement of baggage detection and segmentation is achieved by exploiting the CIELAB color space properties. The proposed system has been extensively tested for its effectiveness, at each stage of improvement, on PETS 2006 dataset and additional CCTV video footage captured to cover specific test scenarios. The experimental results suggest that the proposed algorithm is capable of superseding the functional performance of state-of-the-art baggage detection algorithms.

In this paper we propose an enhanced approach to automatic baggage detection in CCTV video footage that addresses some of the shortcomings of state-of-the-art algorithms and a novel approach to the subsequent recognition of their type. The proposed approach consists of standard steps used in baggage detection, namely, the estimation of moving direction of humans carrying baggage and the alignment of a temporal human-like template with the best matched view-specific exemplars. In addition, unique to the proposed approach is the connected baggage separation algorithm to achieve accurate baggage detection. A further key novel contribution is baggage type classification which is based on the determination of the position of the bag in relevance to the human body carrying it. The proposed system has been extensively tested for its effectiveness on PETS 2006 dataset and additional CCTV video footage captured to cover specific test scenarios. The experimental results suggest that the proposed algorithm is capable of superseding the functional performance of state-of-the art baggage detection algorithms and is able to achieve accurate baggage type classification.