Multi-object tracking of people in surveillance videos, Artur Zygadło @ deepsense.ai

Biography: Currently a Data Scientist at deepsense.ai, working on various problems in the fields of Computer Vision (instance segmentation, multi-object tracking) and Natural Language Processing (document similarity). Having graduated from Warsaw University of Technology, he holds a Bachelor’s degree in Robotics and a Master’s degree in Computer Science. His academic background is in automatic speech recognition for Polish (co-authored two papers) and chatbots (started Ph.D. studies in this area in 2019). His past professional experience includes relational databases (internship at CERN) and data engineering for Samsung’s voice assistant. Abstract: The goal of multi-object tracking is to determine the position of specific objects present in the sequence of video frames. It can be seen as a combination of object detection and re-identification, which can be applied in two separate steps or a single one. For each frame, the visible objects are detected, and then a matching step is performed in order to assign the detections to the already existing tracks or to initiate new ones. This presentation will be the description of deepsense.ai’s ongoing research in the field of multi-object multi-camera tracking of people in surveillance videos in shopping environments. During the talk, our custom tracking pipeline we developed as an extension of state-of-the-art frameworks will be presented, together with the business requirements it needs to follow. The summary of our experiments will be provided. The issues we encountered and our solutions to them will be highlighted, followed by our ideas for further model improvements