Object detection by common fate Hough transform

Two challenging issues for object detection are how to separate near objects and how to separate similar different-class objects. Learned that during human´s vision perception, tokens moving or functioning in a similar manner are perceived as one unit, stated by the common fate principle, we propose a method to detect objects of multiple classes. Our method extends the Implicit Shape Model (ISM) to incorporate motion grouping results of object parts, and meets the challenges. Keypoint-based object parts are firstly detected and then grouped by the similarities of their corresponding trajectories which are traced by keypoint tracking. The grouping results are combined into a Hough transform framework. In Hough transform based methods, each object part votes for object centers and labels according to a trained codebook. In our method, the votes are assigned different weights according to the motion grouping results. One vote is assigned larger weight if it has larger consistence with the votes of other object parts in the same motion group. In such a manner, peaks in the formed Hough images which correspond to object hypotheses become easier to find. And our method gains improvement in both object position and label estimation. Experiments are provided to show the merit in terms of detection accuracy.