Annotation Placement Optimization

Extracting insights from chaos without labeled data. This research project focuses on the unsupervised understanding of surveillance video, tackling the full pipeline from raw pixel processing to user-centric visualization. The core analysis module leverages background modeling to extract foreground entities, constructing trajectory kinematics descriptors to capture motion patterns. By applying unsupervised clustering on these spatiotemporal features, the system automatically distinguishes between normal routines and anomalous events without requiring manual annotations. Beyond detection, my Master’s thesis addressed the challenge of information presentation. I formulated the dynamic annotation placement as a spatiotemporal optimization problem. By enforcing coherence constraints, the algorithm calculates optimal label positions that maximize readability while minimizing occlusion of critical visual information, ensuring a seamless monitoring experience. (Details and visual results to be followed)

Jun 30, 2016