CCTV scene perspective distortion estimation from low-level motion features

Our aim is to estimate the perspective-effected geometric distortion of a scene from a video feed. In contrast to most related previous work, in this task we are constrained to use low-level spatiotemporally local motion features only. This particular challenge arises in many semiautomatic surveillance systems that alert a human operator to potential abnormalities in the scene. Low-level spatiotemporally local motion features are sparse (and thus require comparatively little storage space) and sufficiently powerful in the context of video abnormality detection to reduce the need for human intervention by more than 100-fold. This paper introduces three significant contributions. First, we describe a dense algorithm for perspective estimation, which uses motion features to estimate the perspective distortion at each image locus and then polls all such local estimates to arrive at the globally best estimate. Second, we also present an alternative coarse algorithm that subdivides the image frame into blocks and uses motion features to derive block-specific motion characteristics and constrain the relationships between these characteristics, with the perspective estimate emerging as a result of a global optimization scheme. Third, we report the results of an evaluation using nine large sets acquired using existing closed-circuit television cameras, not installed specifically for the purposes of this paper. Our findings demonstrate that both proposed methods are successful, their accuracy matching that of human labeling using complete visual data (by the constraints of the setup unavailable to our algorithms).

UGI: a multi-dimensional ultrasonic-based interaction approach

We are currently witnessing an era where interaction with computers is no longer limited to conventional methods (i.e. keyboard and mouse). Human Computer Interaction (HCI) as a progressive field of research, has opened up alternatives to the traditional interaction techniques. Embedded Infrared (IR) sensors, Accelerometers and RGBD cameras have become common inputs for devices to recognize gestures and body movements. These sensors are vision based and as a result the devices that incorporate them will be reliant on presence of light. Ultrasonic sensors on the other hand do not suffer this limitation as they utilize properties of sound waves. These sensors however, have been mainly used for distance detection and not with HCI devices. This paper presents our approach in developing a multi-dimensional interaction input method and tool Ultrasonic Gesture-based Interaction (UGI) that utilizes ultrasonic sensors. We demonstrate how these sensors can detect object movements and recognize gestures. We present our approach in building the device and demonstrate sample interactions with it. We have also conducted a user study to evaluate our tool and its distance and micro gesture detection accuracy. This paper reports these results and outlines our future work in the area.

Cloud Base Height Measurement System Based on Stereo Vision with Automatic Calibration

Clouds are important in weather prediction, climate studies and aviation safety. Important parameters include cloud height, type and cover percentage. In this paper, the recent improvements in the development of a low-cost cloud height measurement setup are described. It is based on stereo vision with consumer digital cameras. The cameras positioning is calibrated using the position of stars in the night sky. An experimental uncertainty analysis of the calibration parameters is performed. Cloud height measurement results are presented and compared with LIDAR measurements.