Edge flow

In this paper we introduce a new data driven method to novelty detection and object definition in dynamic video streams that indiscriminately detects both static and moving objects in the scene. A sliding window density estimation is introduced in order to reliably detect texture edges. A Sobel filtering process is used to extract gradient of edges. Using this new approach, the detection of object textures1 can be done accurately and in real-time. In this paper we demonstrate the capabilities of the algorithm on video scenarios, and show that object textures in the scene are reliably detected. We are able to show clearly the capability of the algorithm to be robust in occlusion scenarios; working in real-time, and defining clear objects where other techniques attribute such small detections to noise

Real-time novelty detection in video using background subtraction techniques:state of the art a practical review

Autonomously detecting novelties using background subtraction has quickly become a very important area of image analysis with many different approaches to novelty detection and the output therein. The ultimate goal of the approaches is to be robust to false detections and noise whilst using as little computational power as possible. This review focuses on some of the most prominent pixel-wise background subtraction techniques currently in use, and compares and contrasts their attributes and capabilities. The purpose of this review is to practically summarize the pixel-wise approaches and suggest a way forward from these techniques