Weakly Supervised Video Individual CountingWeakly Supervised Video
  Individual Counting
Xinyan Liu Guorong Li Yuankai Qi Ziheng Yan Zhenjun Han Anton van den Hengel Ming-Hsuan Yang Qingming Huang
Video Individual Counting (VIC) aims to predict the number of unique
individuals in a single video. % Existing methods learn representations based
on trajectory labels for individuals, which are annotation-expensive. % To
provide a more realistic reflection of the underlying practical challenge, we
introduce a weakly supervised VIC task, wherein trajectory labels are not
provided. Instead, two types of labels are provided to indicate traffic
entering the field of view (inflow) and leaving the field view (outflow). % We
also propose the first solution as a baseline that formulates the task as a
weakly supervised contrastive learning problem under group-level matching. In
doing so, we devise an end-to-end trainable soft contrastive loss to drive the
network to distinguish inflow, outflow, and the remaining. % To facilitate
future study in this direction, we generate annotations from the existing VIC
datasets SenseCrowd and CroHD and also build a new dataset, UAVVIC. % Extensive
results show that our baseline weakly supervised method outperforms supervised
methods, and thus, little information is lost in the transition to the more
practically relevant weakly supervised task. The code and trained model will be
public at \href{https://github.com/streamer-AP/CGNet}{CGNet}