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This paper presents1 a multisensor surveillance system used inside an Automated Border Control (ABC) system (more specifically, an eGate). The system consists of two parts: counting the number of persons inside the eGate (person separation), ensuring that no more than one passenger is present; left luggage detection, ensuring that the passenger did not leave any item inside the eGate. These tasks...
The paper presents a method for human detection and tracking in depth images captured by a top-view camera system. We introduce a new feature descriptor which outperforms state-of-the-art features like Simplified Local Ternary Patterns in the given scenario. We use this feature descriptor to train a head-shoulder detector using a discriminative class scheme. A separate processing step ensures that...
We present a GPU-accelerated, real-time and practical, pedestrian detection system, which efficiently computes pedestrian-specific shape and motion cues and combines them in a probabilistic manner to infer the location and occlusion status of pedestrians viewed by a stationary camera. The articulated pedestrian shape is approximated by a mean contour template, where template matching against an incoming...
This work1 presents a real-time hybrid CPU-GPU implementation of a practical people counting system, developed for real-world airport scenarios and using the existing airport single cameras. The cameras are characterized by low quality images and are installed in arbitrary oblique viewing angles and heights relative to the ground plane. The scenes are characterized by large field of view, large scale...
In this work we present an efficient GPU implementation of the Fast Directional Chamfer Matching (FDCM) algorithm [10]. We propose some extensions to the original FDCM algorithm. In particular, we extend the algorithm to handle templates with variable size, to account for perspective effects. To the best of our knowledge, our work is the first to present a full implementation of a shape based matching...
Real-time pedestrian detection in crowded scenarios still represents a major scientific challenge. Dynamic occlusions between humans and the presence of dense gradient structure (clutter) typically render such scenarios complex for automated visual analysis. In this demo we present an algorithmic framework which efficiently computes pedestrian-specific shape and motion cues and combines them in a...
Achieving accurate pedestrian detection for practically relevant scenarios in real-time is an important problem for many applications, while representing a major scientific challenge at the same time. We present a human detection framework which efficiently computes pedestrian-specific shape and motion cues and combines them in a probabilistic manner to infer the location and occlusion status of pedestrians...
Achieving accurate pedestrian detection for practically relevant scenarios in real-time is an important problem for many applications, while representing a major scientific challenge at the same time. In this paper we present an algorithmic framework which efficiently computes pedestrian-specific shape and motion cues and combines them in a probabilistic manner to infer the location and occlusion...
In this paper we present a background subtraction algorithm for a practical surveillance system, on a GPU. It utilizes a compressed non-parametric representation of the history of each pixel, using YCbCr color space, not requiring an offline training period. Although it can be parametrized to cope successfully with moving background, we rather focus on fulfilling some requirements of a practical surveillance...
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