International Journal of Computer Vision
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Principal Axis-Based Correspondence between Multiple Cameras for People Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multicamera People Tracking with a Probabilistic Occupancy Map
IEEE Transactions on Pattern Analysis and Machine Intelligence
People Detection under Occlusion in Multiple Camera Views
SIBGRAPI '08 Proceedings of the 2008 XXI Brazilian Symposium on Computer Graphics and Image Processing
Tracking Multiple Occluding People by Localizing on Multiple Scene Planes
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
A comparative study on multi-person tracking using overlapping cameras
ICVS'13 Proceedings of the 9th international conference on Computer Vision Systems
Hi-index | 0.10 |
This paper proposes a method to locate and track people by combining evidence from multiple cameras using the homography constraint. The proposed method use foreground pixels from simple background subtraction to compute evidence of the location of people on a reference ground plane. The algorithm computes the amount of support that basically corresponds to the ''foreground mass'' above each pixel. Therefore, pixels that correspond to ground points have more support. The support is normalized to compensate for perspective effects and accumulated on the reference plane for all camera views. The detection of people on the reference plane becomes a search for regions of local maxima in the accumulator. Many false positives are filtered by checking the visibility consistency of the detected candidates against all camera views. The remaining candidates are tracked using Kalman filters and appearance models. Experimental results using challenging data from PETS'06 show good performance of the method in the presence of severe occlusion. Ground truth data also confirms the robustness of the method.