N. Al-Shakarji, E. Ufuktepe, F. Bunyak, H. Aliakbarpour, G. Seetharaman, and K. Palaniappan

IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pgs. 1-6, 2020

ground truth generator, object annotation, deep learning, object detection, tracking

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Abstract

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.