COSAR , Serhan

Many supervised approaches report state-of-the-art results for recognizing short-term actions in manually clipped videos by utilizing fine body motion information. The main downside of these approaches is that they are not applicable in real world settings. The challenge is different when it comes to unstructured scenes and long-term videos....

In this paper, a sparsity-driven approach is presented for multi-camera tracking in visual sensor networks (VSNs). VSNs consist of image sensors, embedded processors and wireless transceivers which are powered by batteries. Since the energy and bandwidth resources are limited, setting up a tracking system in VSNs is a challenging problem....

This paper presents an unsupervised approach for learning long-term human activities without requiring any user interaction (e.g., clipping long-term videos into short-term actions, labeling huge amount of short-term actions as in supervised approaches). First, important regions in the scene are learned via clustering trajectory points and the...

Methods for action recognition have evolved considerably over the past years and can now automatically learn and recognize short term actions with satisfactory accuracy. Nonetheless, the recognition of complex activities-compositions of actions and scene objects-is still an open problem due to the complex temporal and composite structure of...

In this paper, we propose a complete framework based on a Hierarchical Activity Models (HAMs) to understand and recognise Activities of Daily Living (ADL) in unstructured scenes. At each particular time of a long-time video, the framework extracts a set of space-time trajectory features describing the global position of an observed person and...

It is well known that video cameras provide one of the richest, and most promising sources of information about people's movements. New technologies which combine video understanding and data-mining can analyse people's behaviour in an efficient way by extracting their trajectories and identifying the main movement flows within a scene equipped...

In this paper we present a unified approach for abnormal behavior detection and group behavior analysis in video scenes. Existing approaches for abnormal behavior detection do either use trajectory based or pixel based methods. Unlike these approaches, we propose an integrated pipeline that incorporates the output of object trajectory analysis...

Visual activity recognition plays a fundamental role in several research fields as a way to extract semantic meaning of images and videos. Prior work has mostly focused on classification tasks, where a label is given for a video clip. However, real life scenarios require a method to browse a continuous video flow, automatically identify...