We introduce a new large-scale video dataset designed to assess the performance of diverse visual event recognition algorithms with a focus on continuous visual event recognition (CVER) in outdoor areas with wide coverage. Previous datasets for action recognition are unrealistic for real-world surveillance because they consist of short clips showing one action by one individual [15,8]. Datasets have been developed for movies [11] and sports [12], but, these actions and scene conditions do not apply effectively to surveillance videos. Our dataset consists of many outdoor scenes with actions occurring naturally by non-actors in continuously captured videos of the real world. The dataset includes large numbers of instances for 23 event types distributed throughout 29 hours of video. This data is accompanied by detailed annotations which include both moving object tracks and event examples, which will provide solid basis for large-scale evaluation. Additionally, we propose different types of evaluation modes for visual recognition tasks and evaluation metrics along with our preliminary experimental results. We believe that this dataset will stimulate diverse aspects of computer vision research and help us to advance the CVER tasks in the years ahead.

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The iris is a highly accurate biometric identifier. However widespread adoption is hindered by the difficulty of capturing high-quality iris images with minimal user cooperation. This paper describes a first-generation prototype iris identification system designed for stand-off cooperative access control. This system identifies individuals who stand in front of and face the system after 3.2 seconds on average. Subjects within a capture zone are imaged with a calibrated pair of wide-field-of-view surveillance cameras. A subject is located in three dimensions using face detection and triangulation. A zoomed near infrared iris camera on a pan-tilt platform is then targeted to the subject. The iris camera lens has its focal distance automatically adjusted based on the subject distance. Integrated with the iris camera on the pan-tilt platform is a near infrared illuminator that is composed of an array of directed LEDs. Video frames from the iris camera are processed to detect and segment the iris, generate a template and then identify the subject.

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