Weakly Supervised Video Individual CountingWeakly Supervised Video Individual Counting

• URL: http://arxiv.org/abs/2312.05923v1
• Date: Sun, 10 Dec 2023 16:12:13 GMT
• Title: Weakly Supervised Video Individual CountingWeakly Supervised Video Individual Counting
• Authors: Xinyan Liu and Guorong Li and Yuankai Qi and Ziheng Yan and Zhenjun Han and Anton van den Hengel and Ming-Hsuan Yang and Qingming Huang
• Abstract summary: Video Individual Counting aims to predict the number of unique individuals in a single video. We introduce a weakly supervised VIC task, wherein trajectory labels are not provided. In doing so, we devise an end-to-end trainable soft contrastive loss to drive the network to distinguish inflow, outflow, and the remaining.
• Score: 126.75545291243142
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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}

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