Optical Flow Based Detection in Mixed Human Robot Environments

In this paper we compare several optical flow based features in order to distinguish between humans and robots in a mixed human-robot environment. In addition, we propose two modifications to the optical flow computation: (i) a way to standardize the optical flow vectors, which relates the real world motions to the image motions, and (ii) a way to improve flow robustness to noise by selecting the sampling times as a function of the spatial displacement of the target in the world. We add temporal consistency to the flow-based features by using a temporalBoost algorithm. We compare combinations of: (i) several temporal supports, (ii) flow-based features, (iii) flow standardization, and (iv) flow sub-sampling. We implement the approach with better performance and validate it in a real outdoor setup, attaining real-time performance.