CYCLEPTZ: THE LEARNING-BASED CONTROL METHOD FOR MASTER-SLAVE CAMERA SYSTEMS

A master-slave setup consists of fixed and PTZ cameras monitoring a scenario to provide high-resolution images of target regions. Most of the works in literature focus on the unrealistic setting with a single person and single ground plane. Differently, in this work we proposed the CyclePTZ, a learning-based method that learns the mappings between master-slave and slave-master using a cycle-consistent neural network. While the master-slave mapping is used to control the PTZ cameras, the slave-master works as a supplementary supervisor for network training and to perform hard sample mining. More importantly, as both functions are learned simultaneously using the cycle loss, the CyclePTZ is able to learn a better mapping between fixed and PTZ cameras. Experimental results demonstrate that the proposed CyclePTZ is able to follow targets in multiple ground planes and to record corresponding points with multiple people in the scene. We compare the proposed method with the current literature in real-time experiments that demonstrate favorable performance of CyclePTZ (i.e., reducing the target center error in 10.92 percentage points).