The detection of abnormal behaviours in crowded scenes has to deal with many challenges. This paper presents an efficient method for detection and localization of anomalies in videos. Using fully convolutional neural networks (FCNs) and temporal data, a pre-trained supervised FCN is transferred into an unsupervised FCN ensuring the detection of (global) anomalies in scenes. High performance in terms of speed and accuracy is achieved by investigating the cascaded detection as a result of reducing computation complexities. This FCN-based architecture addresses two main tasks, feature representation and cascaded outlier detection. Experimental results on two benchmarks suggest that detection and localization of the proposed method outperforms existing methods in terms of accuracy.

In this work, an algorithm is proposed to scramble an JPEG compressed image without causing bitstream size expansion. The causes of bitstream size expansion in the existing scrambling methods are first identified. Three recommendations on AC coefficients in the scrambled image are proposed to combat unauthorized viewing. As the first step of the scrambling algorithm, edges are identified directly in the frequency domain using solely AC coefficients without relying on any traditional methods. These edges then form a low resolution image of its original counterpart and the information is utilized to identify regions. The DC coefficients are encoded in region-basis to suppress bitstream size expansion while achieving scrambling effect. Experiments were carried out to verify the basic performance of the proposed scrambling method. For the parameter settings considered, most of the scrambled images are of smaller bitstream size than their original counter parts.

No abstract

No abstract

No abstract