  We present a pedestrian tracking algorithm, DensePeds, that tracks
individuals in highly dense crowds (greater than 2 pedestrians per square
meter). Our approach is designed for videos captured from front-facing or
elevated cameras. We present a new motion model called Front-RVO (FRVO) for
predicting pedestrian movements in dense situations using collision avoidance
constraints and combine it with state-of-the-art Mask R-CNN to compute sparse
feature vectors that reduce the loss of pedestrian tracks (false negatives). We
evaluate DensePeds on the standard MOT benchmarks as well as a new dense crowd
dataset. In practice, our approach is 4.5 times faster than prior tracking
algorithms on the MOT benchmark and we are state-of-the-art in dense crowd
videos by over 2.6% on the absolute scale on average.
