Non-intrusive Head Movement Analysis of Videotaped Seizures of Epileptic Origin

Abstract — In this work we propose a non-intrusive video analytic system for patient’s body parts movement analysis in Epilepsy Monitoring Unit. The system utilizes skin color modeling, head/face pose template matching and face detection to analyze and quantify the head movements. Epileptic patients’ heads are analyzed holistically to infer seizure and normal random movements. The patient does not require to wear any special clothing, markers or sensors, hence it is totally nonintrusive. The user initializes the person-specific skin color and selects few face/head poses in the initial few frames. The system then tracks the head/face and extracts spatio-temporal features. Support vector machines are then used on these features to classify seizure-like movements from normal random movements. Experiments are performed on numerous long hour video sequences captured in an Epilepsy Monitoring Unit at a local hospital. The results demonstrate the feasibility of the proposed system in pediatric epilepsy monitoring and seizure detection. I

Video object detection, segmentation and motion trajectory extraction over compressed domain

This thesis focused on low-level visual feature extraction over compressed bitstreams, containing macroblock motion vectors (MVs) and DCT coefficients.Doctor of Philosophy (EEE

Learning video manifolds for content analysis of crowded scenes

In this paper, we propose a new approach for recognizing group events and abnormality detection in a crowded scene. A manifold learning algorithm with temporal-constraints is proposed to embed a video of a crowded scene in a low-dimensional space. Our low dimensional representation of a video preserves the spatial temporal property of a video as well as the characteristic of the video. Recognizing video events and abnormality detection in a crowded scene is achieved by studying the video trajectory in the manifold space. We evaluate our proposed method on the state-of-the-art public data-sets containing different crowd events. Qualitative and quantitative results show the promising performance of the proposed method. © 2012 Information Processing Society of Japan

Automatic analysis of fish behaviors and abnormality detection

Current state-of-the-art fish monitoring systems are lack of intelligent in interpreting fishes behaviors automatically. To tackle these problems, we propose a vision-based method that automatically analyze behaviors of a group of fishes in an aquarium and detect abnormality precisely. Here we consider the problem in two steps. First, we propose a new incremental spectral clustering method to extract frequently occurred key swimming patterns of fishes. Then, we present video sequences of fishes into a trajectory through the space of these key patterns. Studying these trajectories provides a new tool to analyze fishes behaviors. Comparisons of fishes behaviors in clean water and water in the presence of chemicals provides a new tool to detect any abnormality. Experimental results illustrate that the precision value of our proposed method is above 90%.

Intelligent monitoring of complex environments

Many countries around the world have implemented or are in the process of implementing tighter security measures in public and private places. Such measures are becoming widespread and are applied not only at government, military, and corporate facilities, but also in civilian infrastructures such as railway and bus stations, concourses, hospitals, nursing homes, playgrounds, and so forth. Stricter monitoring implies the use of a larger number of sensors, surveillance networks, and platforms, all of which generate large volumes of data that must be processed. A decade ago, surveillance systems had to deal with terabytes of video information; now we must talk of petabytes and soon higher orders of magnitude. Therefore, it is of utmost importance that we devise intelligent monitoring systems that can filter data and extract only relevant information and knowledge about the monitored scene. Such systems must implement algorithms that can automatic understand events, detect anomalous activity, generate an immediate response in emergency cases, and intelligently archive information for post-event analysis and system update. The articles in this special issue consider different aspects of the development of modern and intelligent systems for monitoring complex environments