Abnormality detection Motion detection

a m enti iou eatu second phase, the co-occurrence matrix is used as a potential function in a Markov random field frame-level lo etectio ving ob malities from a raw video? Some approaches have been proposed to perform such low-level abnormality detection (Adam et al., 2008; Jodoin et al., 2008). Nevertheless, we point out that these methods process each pixel independently and thus ignore spatial correlation across space and time. These correlations may not only be important in improving false alarms and misses but also in detecting abnormal-restricted-area access detection (Konrad, 2005), car counting (Friedman and Russell, 1997), detection of people carrying cases (Haritaoglu et al., 2000), abandoned objects detection (Smith et al., 2006; Lim et al., 2006), action recognition (Ahmad and Lee, 2008), plate recognition, group detection, etc. These methods clearly focus on finding good matches between objects in a video and known templates stored in a database. By their nature, shape recognition methods require a list of objects or behavior patterns that are anomalous. Unfortunately, this is not always possible, especially when suspicious activities cannot be known a priori. An alternative approach advocated in recen