Improving multi-view object recognition by detecting changes in point clouds

This paper proposes the use of change detection in a multi-view object recognition system in order to improve its flexibility and effectiveness in dynamic environments. Multi-view recognition approaches are essential to overcome problems related to clutter, occlusion or camera noise, but the existing systems usually assume a static environment. The presence of dynamic objects raises another issue - the inconsistencies introduced to the internal scene model. We show that by incorporating the change detection and correction of the inherent scene inconsistencies, we reduce false positive detections by 70% in average for moving objects when tested on the publicly available TUW dataset. To reduce time required for verifying a large set of accumulated object pose hypotheses, we further integrate a clustering approach into the original multi-view object recognition system and show that this reduces computation time by 16%.