A Query Language Combining Object Features and Semantic Events for Surveillance Video Retrieval

International audienceIn this paper, we propose a novel query language for video indexing and retrieval that (1) enables to make queries both at the image level and at the semantic level (2) enables the users to define their own scenarios based on semantic events and (3) retrieves videos with both exact matching and similarity matching. For a query language, four main issues must be addressed: data modeling, query formulation, query parsing and query matching. In this paper we focus and give contributions on data modeling, query formulation and query matching. We are currently using color histograms and SIFT features at the image level and 10 types of events at the semantic level. We have tested the proposed query language for the retrieval of surveillance videos of a metro station. In our experiments the database contains more than 200 indexed physical objects and 48 semantic events. The results using different types of queries are promising

A novel framework for intelligent surveillance system based on abnormal human activity detection in academic environments

Abnormal activity detection plays a crucial role in surveillance applications, and a surveillance system thatcan perform robustly in an academic environment has become an urgent need. In this paper, we propose a novel framework for an automatic real-time video-based surveillance system which can simultaneously perform the tracking, semantic scene learning, and abnormality detection in an academic environment. To develop our system, we have divided the work into three phases: preprocessing phase, abnormal human activity detection phase, and content-based image retrieval phase. For motion object detection, we used the temporal-differencing algorithm and then located the motions region using the Gaussian function.Furthermore, the shape model based on OMEGA equation was used as a filter for the detected objects (i.e.,human and non-human). For object activities analysis, we evaluated and analyzed the human activities of the detected objects. We classified the human activities into two groups:normal activities and abnormal activities based on the support vector machine. The machine then provides an automatic warning in case of abnormal human activities. It also embeds a method to retrieve the detected object from the database for object recognition and identification using content-based image retrieval.Finally,a software-based simulation using MATLAB was performed and the results of the conducted experiments showed an excellent surveillance system that can simultaneously perform the tracking, semantic scene learning, and abnormality detection in an academic environment with no human intervention

Acquiring multi-scale images by pan-tilt-zoom control and automatic multi-camera calibration

This paper describes a system for automatically acquiring high-resolution images by steering a pan-tilt-zoom camera at targets detected in a fixed camera view. The system uses a novel method to automatically calibrate between multiple cameras, estimating the homography between the cameras in a home position, together with the effects of pan and tilt controls and the expected height of a person in the image. These calibrations are chained together to steer a slave camera. In addition we describe a simple manual calibration scheme. 1

Comparison of Sequence Matching Techniques for Video Copy Detection

Video copy detection is a complementary approach to watermarking. As opposed to watermarking, which relies on inserting a distinct pattern into the video stream, video copy detection techniques match content-based signatures to detect copies of video. Existing typical content-based copy detection schemes have relied on image matching. This paper proposes two new sequence-matching techniques for copy detection and compares the performance with one of the existing techniques. Motion, intensity and color-based signatures are compared in the context of copy detection. Results are reported on detecting copies of movie clips

Systems and methods for analyzing spatiotemporally ambiguous events

Principles of the invention provide techniques for analyzing spatiotemporally ambiguous event