Re-identifying the same person in different images is a distinct challenge for visual surveillance systems. Building an accurate correspondence between highly variable images requires a suitable dissimilarity measure. To date, most existing measures have used adapted distance based on a learned metric. Unfortunately, real-world human image data, which tends to show large intra-class variations and small inter-class differences, continues to prevent these measures from achieving satisfactory re-identification performance. Recognizing neighboring distribution can provide additional useful information to help tackle the deviation of the to-be-measured samples, we propose a novel dissimilarity measure from the neighborhood-wise relative information perspective, which can deliver the effectiveness of those well-distributed samples to the badly-distributed samples to make intra-class dissimilarities smaller than inter-class dissimilarities, in a learned discriminative space. The effectiveness of this method is demonstrated by explanation and experimentation.

Multiple-shot person re-identification, which is valuable for application in visual surveillance, tackles the problem of building the correspondence between images of the same person from different cameras. It is challenging because of the large within-class variations due to the changeable body appearance and environment and the small between-class differences arising from the possibly similar body shape and clothes style. A novel method named “Bi-level Relative Information Analysis” is proposed in this paper for the issue by treating it as a set-based ranking problem. It creatively designs a relative dissimilarity using set-level neighborhood information, called “Set-level Common-Near-Neighbor Modeling”, complementary to the sample-level relative feature “Third-Party Collaborative Representation” which has recently been proven to be quite effective for multiple-shot person re-identification. Experiments implemented on several public benchmark datasets show significant improvements over state-of-the-art methods.

A novel approach for detecting anomaly in visual surveillance system is proposed in this paper. It is composed of three parts: (a) a dense motion field and motion statistics method, (b) motion directional PCA for feature dimensionality reduction, (c) an improved one-class SVM for one-class classification. Experiments demonstrate the effectiveness of the proposed algorithm in detecting abnormal events in surveillance video, while keeping a low false alarm rate. Our scheme works well in complicated situations that common tracking or detection modules cannot handle.

This paper introduces an intermediate virtual representation, called ideal fisheye image, which obeys the ideal simple projection without camera distortion. By using a look-up-table from the ideal fisheye image to the input fisheye image with distortion, a view-direction-free perspective display can be generated fast in comparison with the method of solving a set of nonlinear equations of camera distortion parameters.

This paper proposes a methodology for detecting matte-surfaced objects on a scene using color information and spatial thresholding. First, a difference image is obtained via a pixel-wise comparison of the color content of a 'clean' reference image and a sample image. Then, spatial thresholding of the difference image is performed to extract any objects of interest, followed by morphological post-processing to remove pixel noise. We study the applicability of two alternate color spaces (HSV, CIE Lab) for computing the difference image. Similarly, we employ two spatial thresholding methods, which determine the global threshold from the local spatial properties of the difference image. We demonstrate the performance of the proposed approach in scene surveillance, where the objective is to monitor a shipping dock for the appearance of needless objects such as cardboard boxes. In order to analyze the robustness of the approach, the experiment includes three different types of scenes categorized as 'easy,' 'moderate,' and 'difficult,' based on properties such as heterogeneity of the background, existence of shadows and illumination changes, and reflectivity and chroma properties of the objects. The experimental results show that relatively good recognition accuracy is achieved on 'easy' and 'moderate' scenes, whereas 'difficult' scenes remain a challenge for future work.