Visual Path Prediction in Complex Scenes with Crowded Moving Objects

Young Joon Yoo, Kimin Yun, Sangdoo Yun, Jonghee Hong, Hawook Jeong, Jin Young Choi

Abstract: This paper proposes a novel path prediction algorithm for progressing one step further than the existing works focusing on single target path prediction. In this paper, we consider moving dynamics of co-occurring objects for path prediction in a scene that includes crowded moving objects. To solve this problem, we first suggest a two-layered probabilistic model to find major movement patterns and their cooccurrence tendency. By utilizing the unsupervised learning results from the model, we present an algorithm to find the future location of any target object. Through extensive qualitative/quantitative experiments, we show that our algorithm can find a plausible future path in complex scenes with a large number of moving objects.