In this paper, a linear self-calibration approach for camera focal length estimation is proposed. No priori information of camera motion is needed for the method. The unknown focal length of camera is conveniently obtained in closed form from degenerated Kruppa's equations. These degenerated equations (one quadratic and two linear) provide the essential constraints on calibration of camera intrinsic parameters. Real images of indoor and outdoor generic scenes and 3D metric reconstruction are used to analyze the accuracy of the estimated focal length. Experiments results show that the method is both robust and efficient.

We describe a computationally inexpensive but effective scheme for automatic transcription of polyphonic music. Based on a two-step strategy, tracks creation and tracks grouping, the scheme utilizes innovative comb-filtering and 'sharpening' steps to produce the desired transcription output in the form of discrete notes with temporal, pitch and amplitude attributes. Recall and precision measurements are used to analyze and quantify transcription accuracy. Promising results are achieved for the automatic transcription of both synthetic and acoustic piano music.

An adaptive spatio-temporal filtering scheme based on a novel concept of motion frequency is proposed as the preprocessing step to enhance human detection under a noisy aquatic environment. In this framework, each pixel is first classified into one of three categories quantified by its motion frequency, each of which is filtered using an appropriate filtering scheme. For regions affected by glistening reflections and glares, a color compensation filter is specially developed to improve partly hidden human detection and minimize errors due to moving background elements. Additionally, a blob-based verification procedure is introduced to remove the target's shadow. Experimental results demonstrate the effectiveness of the algorithm and its role in enhancing the robustness of an aquatic surveillance system for outdoor swimming pools at nighttime.

Automatically understanding events happening at a site is the ultimate goal of visual surveillance system. We investigate the challenges faced by automated surveillance systems operating in hostile conditions and demonstrate the developed algorithms via a system that detects water crises within highly dynamic aquatic environments. An efficient segmentation algorithm based on robust block-based background modelling and thresholding-with-hysteresis methodology enables swimmers to be reliably detected amid reflections, ripples, splashes and rapid lighting changes. Partial occlusions are resolved using a Markov Random Field framework that enhances the tracking capability of the system. Visual indicators of water crises are identified based on professional knowledge of water crises detection, based on which a set of swimmer descriptors has been defined. Through seamlessly fusing the extracted swimmer descriptors based on a novel functional link network, the system achieves promising results for water crises detection. The developed algorithms have been incorporated into a live system with robust performance for different hostile environments faced by an outdoor swimming pool.