产品 求购 供应 文章 问题

0431-81702023
光学工程
一个基于鱼眼摄像机的自动监控系统

Automatic surveillance system using f ish-eye lens camera

This letter presents an automatic surveillance system using fish-eye lens camera. Our system achieves wide-area automatic surveillance without a dead angle using only one camera. We propose a new human detection method to select the most adaptive classifier based on the locations of the human candidates. Human regions are detected from the fish-eye image effectively and are corrected for perspective versions. An experiment is performed on indoor video sequences with different illumination and crowded conditions, with results demonstrating the efficiency of our algorithm.

    OCIS codes: 110.0110, 100.0100, 150.0150.

    doi: 10.3788/COL201109.021101.

    Due to large field of view, wide-angle lens are popularly used for various applications, such as surveillance, robotic navigation, and semi-automatic parking systems. Because the angle of view of the fish-eye lens used in our system was up to 185? , it achieved effective widearea surveillance without a dead angle only one camera. However, it brought an inherent distortion in the image, and this distorted image must be rectified or restored in order to recognize and understand the image accurately. Human detection and tracking is a necessary approach for automatic surveillance systems. However, in the image taken by our surveillance system, the region where human enters the surveillance space is distorted and it is difficult to detect humans using the original method introduced in Refs. [1?9]. To our knowledge, there is still no reliable pedestrian detection algorithm reported for fish-eye image. Refernece [10] proposed human detection method using fish-eye image to detect ellipses from the subtraction images of fish-eye pictures as human area. However, in a more crowded situation and when sudden illumination changes occur, their method shows a clear increase in false alarm rate.

   In order to improve the efficiency of human detection on fish-eye images even in crowded indoor environments, we propose a human detection method. The rotations and sizes of the human regions on the fish-eye image change based on the locations of humans in the surveillance area. We propose a method to normalize these regions. Because a fish-eye lens camera is set on top of the surveillance area, the shapes of humans are changed based on their locations in the surveillance area. In this letter, we create three types of classifiers to detect humans in any part of the surveillance area; the most adaptive classifier for each human is chosen automatically from several classifiers. Moreover, we propose a method to minimize the occlusion effects. We infer the possible occlusion region in each human candidate region based on its location on the fish-eye image. Once the occluded regions are detected, the occlusion effects can be minimized by adjusting the threshold of the classifier.

   Unlike other systems such as those proposed in Refs. [11,12], the human regions in our proposed method are detected initially from the fish-eye image, and only the human regions are corrected afterwards. In other systems, the entire input fish-eye images are corrected first and then the human regions are detected from the corrected images. Using our system, the processing efficiency can be improved and the processing time can be significantly reduced.

   The system is designed as illustrated in Fig. 1, wherein the fish-eye lens camera is set on top of the surveillance area. The input image of the fish-eye lens camera is illustrated in Fig. 2, with the background image illustrated in Fig. 2(a) and the input image illustrated in Fig. 2(b).

    The edges of the background and input images are extracted using Sobel operator[13] as illustrated in Figs. 3(a) and (b). In addition, the subtraction image between the input edge image and the background edge image is computed, as illustrated in Fig. 3(c). As shown in Fig. 3, all the head edges look like ellipses; thus, an efficient ellipse detection method[14] is adopted to extract the el lipses from the edge image as head candidates. The proposed method is presented as follows.

 

For each pair of pixels, (x1, y1) and (x2, y2), the following five parameters of an ellipse can be calculated:

where (x0, y0) is the center of the assumed ellipse, a is the half-length of the major axis, α is the orientation of the ellipse, f is the focus of the ellipse, b is the half-length of the minor axis, and d is the distance between (x, y) and (x0, y0). A one-dimensional accumulator array is then used to vote on the half-length of the minor axis; if the votes reach a threshold, an ellipse is found and we yield the parameters for the detected ellipse and remove all pixels on that ellipse from the image.

   The results of the extracted head candidates are illustrated in Fig. 4. The following process will be executed for each head candidate.

    Based on the location of the head candidate, the method introduced in Ref. [15] was adopted in this letter in order to determine the size of the human candidates in different locations. Considering a cube whose size is bigger than a normal man standing on the floor in the real world, the projection of the cube can be considered as the human candidate region when the coordinate of the upper cube’s projection is near the head candidate.

 

   As shown in Fig. 1, points P 0 1 , P 0 2 , and P 0 3 in the fish eye image are the projections of points P1, P2, and P3 in real word, respectively. The projections of humans A and B are illustrated in Fig. 5. All the projections of humans seem to stand on the line la between the center of the fish-eye image (O) and their head candidate center (P 0 2 and P 0 3 ); the feet of the human (P 0 1 ) are always closer to the center of the fish-eye image (O) than the human’s head (P 0 2 ). The angles α between the vertical