[show abstract][hide abstract] ABSTRACT: Visual surveillance is an active research topic in image processing. Transit systems are actively seeking new or improved ways to use technology to deter and respond to accidents, crime, suspicious activities, terrorism, and vandalism. Human behavior-recognition algorithms can be used proactively for prevention of incidents or reactively for investigation after the fact. This paper describes the current state-of-the-art image-processing methods for automatic-behavior-recognition techniques, with focus on the surveillance of human activities in the context of transit applications. The main goal of this survey is to provide researchers in the field with a summary of progress achieved to date and to help identify areas where further research is needed. This paper provides a thorough description of the research on relevant human behavior-recognition methods for transit surveillance. Recognition methods include single person (e.g., loitering), multiple-person interactions (e.g., fighting and personal attacks), person-vehicle interactions (e.g., vehicle vandalism), and person-facility/location interactions (e.g., object left behind and trespassing). A list of relevant behavior-recognition papers is presented, including behaviors, data sets, implementation details, and results. In addition, algorithm's weaknesses, potential research directions, and contrast with commercial capabilities as advertised by manufacturers are discussed. This paper also provides a summary of literature surveys and developments of the core technologies (i.e., low-level processing techniques) used in visual surveillance systems, including motion detection, classification of moving objects, and tracking.
IEEE Transactions on Intelligent Transportation Systems 04/2010; · 3.06 Impact Factor
[show abstract][hide abstract] ABSTRACT: A novel thin line detection algorithm for use in low-altitude aerial vehicles is presented. This algorithm is able to detect thin obstacles such as cables, power lines, and wires. The system is intended to be used during urban search and rescue operations, capable of dealing with low-quality images, robust to image clutter, bad weather, and sensor artifacts. The detection process uses motion estimation at the pixel level, combined with edge detection, followed by a windowed Hough transform. The evidence of lines is tracked over time in the resulting parameter spaces using a dynamic line movement model. The algorithm's receiver operating characteristic curve (ROC) is shown, based on a multi-site dataset with 86 videos with 10160 wires spanning in 5576 frames.
IEEE Transactions on Aerospace and Electronic Systems 08/2009; · 1.30 Impact Factor
[show abstract][hide abstract] ABSTRACT: In this paper, we propose a new edge-based text verification approach for video. Based on the investigation of the relation between candidate blocks and their neighbor areas, the proposed approach first detects background edges in candidate blocks, then erases them by an edge tracking technique, and finally the candidate blocks containing too few remaining edges are eliminated as false alarms. Three measures for text detection evaluation in video were used to assess the performance of the proposed text verification approach. Experimental results on 50 broadcast news video clips demonstrate the validity of our approach.
Pattern Recognition, 2008. ICPR 2008. 19th International Conference on; 01/2009
[show abstract][hide abstract] ABSTRACT: This paper describes a vision-based street detection algorithm to be used by small autonomous aircraft in low-altitude urban surveillance. The algorithm uses Bayesian analysis to differentiate between street and background pixels. The color profile of edges on the detected street is used to represent objects with respect to their surroundings. These color profiles are used to improve street detection over time. Pixels that do not likely originate from the "true" street are excluded from the recurring Bayesian estimation in the video. Results are presented comparing to a previously published Unmanned Aerial Vehicle (UAV) road detection algorithm. Robust performance is demonstrated with urban surveillance scenes including UAV surveillance, police chases from helicopters, and traffic monitoring. The proposed method is shown to be robust to data uncertainty and has low sensitivity to the training dataset. Performance is computed using a challenging multi-site dataset that includes compression artifacts, poor resolution, and large variation of scene complexity.