A framework for inter-camera association of multi-target trajectories by invariant target models

We propose a novel framework for associating multi-target trajectories across multiple non-overlapping views (cameras) by constructing an invariant model per each observed target. Ideally, these models represent the targets in a unique manner. The models are constructed by generating synthetic images that simulate how targets would be seen from different viewpoints. Our framework does not require any training or other supervised phases. Also, we do not make use of spatiotemporal coordinates of trajectories, i.e., our framework seamlessly works with both overlapping and non-overlapping field-of-views (FOVs) as well as widely separated ones. Also, contrary to many other related works, we do not try to estimate the relationship between cameras that tends to be error prone in environments like airports or supermarkets where targets wander about different areas, stop at times, or turn back to their starting location. We show the results obtained by our framework on a rather challenging dataset. Also, we propose a black-box approach based on Support Vector Machine (SVM) for fusing multiple pertinent algorithms and demonstrate the added value of our framework with respect to some basic techniques.