An Efficient Crossing-Line Crowd Counting Algorithm with Two-Stage Detection

Abstract

Crowd counting is a challenging task in crowded scenes due to heavy occlusions, appearance variations and perspective distortions. Current crowd counting methods typically operate on an image patch level with overlaps, then sum over the patches to get the final count. In this paper we describe a real-time pedestrian counting framework based on a two-stage human detection algorithm. Existing works with overhead cameras is mainly based on visual tracking, and their robustness is rather limited. On the other hand, some works, which focus on improving the performances, are too complicated to be realistic. By adopting a line sampling process, a temporal slice image can be obtained for pedestrian counting without the need for visual tracking. Only ten low level features are extracted from the input image to establish a feature vector. As a result, our algorithm is more efficient and accurate than existing methods. Pedestrians in the temporal slice image are then located by the two-stage detection algorithm, which is largely based on support vector machine and affinity propagation clustering. Moreover, a novel algorithm is proposed to determine the moving directions of pedestrians by comparing the centers of them in two temporal slice images. Extensive experiments reveal that our system achieves satisfaction performances in terms of both robustness and efficiency.

Surveillance Video Key Frame Extraction Based on Center Offset

“…As one of the most fundamental and challenging problems in object extraction, object classification [24][25][26], object tracking [27], crowd counting [28] and object recognition [29], objection detection has attracted considerable attention in recent years. Many papers on moving object detection have been published.…”

Adaptive Scheme for Crowd Counting Using off-the-Shelf Wireless Routers

“…Since the outbreak of the world-wide novel coronavirus pandemic, crowd counting in public areas, such as in shopping centers and in commercial streets, has gained popularity among public health administrations for preventing the crowds from gathering. Traditionally, image-based methods are most often used to estimate the human crowd count, but they are limited to the illumination intensity of environment, line-of-sight propagation property of light, and the public consideration of privacy [9][10][11][12][13][14][15][16][17][18]. In this paper, we introduce an adaptive model for human crowd count estimation by exploiting rich CSI data embedded in 802.11n Wi-Fi networks.…”