Detecting Pedestrians Using Patterns of Motion and Appearance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
View independent vehicle/person classification
Proceedings of the ACM 2nd international workshop on Video surveillance & sensor networks
A Model-Based Vehicle Segmentation Method for Tracking
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Real-time foreground-background segmentation using codebook model
Real-Time Imaging
A cascade of feed-forward classifiers for fast pedestrian detection
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
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Existing pedestrian and vehicle detection algorithms use 2D cues of objects, such as pixel values, color, texture, shape information or motion. The use of 3D cues in object detection, on the other hand, is not well studied in the literature. In this paper, we propose an efficient algorithm that detects pedestrian and vehicle using their 3D cues. The proposed algorithm first detects moving objects in a video frame using a background modeling technique. For each moving object, we extract its width and height in 3D space, with the aid of the intrinsic and extrinsic parameters of the camera monitoring the scene. To estimate the camera parameters, we apply a calibration-free method, which simply requires users to specify six vertices on a cuboid in the scene. Then based on its 3D cues, a object is verified whether it is a pedestrian(vehicle) or not by the class-specific Support Vector Machine (SVM). In our experiment, the proposed algorithm achieves a precision of 88.2%(89.1%) for pedestrian(vehicle) detection, at 32 frame-per-second on average upon five testing sequences.