In recent years, deep learning algorithms have achieved top performances in object detection tasks. However, in real-time, systems having memory or computing limitations very wide and deep networks with numerous parameters constitute a major obstacle. In this paper, we propose a fast method for detecting pedestrians in surveillance systems having limited memory and processing units. Our proposed method applies a model compression technique based on a teacher-student framework to a random forest (RF) classifier instead of a wide and deep network because a compressed deep network still demands a large memory for a large amount of parameters and processing resources for multiplication. The first objective of the proposed compression method is to train a student shallow RF (S-RF), which can mimic the teacher RF's performance, by using a softened version of the teacher RF's output. Second, a deep network cannot easily detect small and closely located pedestrians in a surveillance video captured from a high perspective because of frequent convolutions and pooling processes. In this paper, adaptive image scaling and region of interest with S-RF were therefore combined to allow fast and accurate pedestrian detection in a low-specification surveillance system. In experiments, our proposed method achieved up to a 2.2 times faster speed and a 2.68 times higher compression rate than teacher RF and a better detection performance than several stateof-the-art methods on the Performance Evaluation of Tracking and Surveillance 2006, Town Centre, and Caltech benchmark datasets.

With the rapid development of information technology, natural disaster prevention is growing as a new research field dealing with surveillance systems. To forecast and prevent the damage caused by natural disasters, the development of systems to analyze natural disasters using remote sensing geographic information systems (GIS), and vision sensors has been receiving widespread interest over the last decade. This paper provides an up-to-date review of five different types of natural disasters and their corresponding warning systems using computer vision and pattern recognition techniques such as wildfire smoke and flame detection, water level detection for flood prevention, coastal zone monitoring, and landslide detection. Finally, we conclude with some thoughts about future research directions.Ko and Kwak: Survey of computer vision-based natural disaster warning systems Optical Engineering 070901-11 July 2012/Vol. 51 (7) Ko and Kwak: Survey of computer vision-based natural disaster warning systems

We propose an algorithm for abnormal event detection in surveillance video. The proposed algorithm is based on a semiunsupervised learning method, a kind of feature-based approach so that it does not detect the moving object individually. The proposed algorithm identifies dominant flow without individual object tracking using a latent Dirichlet allocation model in crowded environments. It can also automatically detect and localize an abnormally moving object in real-life video. The performance tests are taken with several real-life databases, and their results show that the proposed algorithm can efficiently detect abnormally moving objects in real time. The proposed algorithm can be applied to any situation in which abnormal directions or abnormal speeds are detected regardless of direction. C 2011 Society of Photo-Optical Instrumentation Engineers (SPIE).

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