Semi-automatic System for Rapid Annotation of Moving Objects in Surveillance Videos using Deep Detection and Multi-object Tracking Techniques

With the recent advances in video sensor technologies, emergence of new applications associated with these technologies, and demand for automated video analytics have increased the need for ground-truth annotations. Researchers attempt to explore different methodologies and algorithms on different challenging datasets. Ground-truth annotations are needed for quantitative evaluation and comparison of video analysis methods, and training machine learning approaches. Ground Truth Generator (GTG) is our semi-automatic solution for rapid moving object annotation. GTG allows annotation of high densities of moving objects in a video with considerably fewer human interactions, and greatly reduces annotation effort and time. For each individual moving object in a video GTG generates bounding boxes, object classes, and motion trajectories. GTG integrates a fully-automated detection, tracking, and fusion pipeline with an interactive visualization, annotation, and editing interface. Our proposed system was tested on FPSS [1] and VisDrone [2] datasets and showed promising results.