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However, SFU-HW-Objects-v1 is not suitable for tracking purposes because there is no annotation distinguishing different objects from the same class. Therefore, we further created unique Object IDs within each class, which enables distinction of different objects in each class. Further, the same Object ID is used for the same object in different frames, which allows computing tracking metrics. These object IDs are included in the second column of the provided annotation files. 1), we computed NCC for all possible pairs of object bounding boxes between these two frames. 1 that gave the highest NCC score. 1 after visual inspection. 1. Such situation could occur when an object disappears and re-appears due to occlusion, or appears for the first time. After assigning the Object IDs in the current frame, the annotation process proceeded to the next frame. Figure 3 summarizes the semi-automated process of assigning Object IDs. No human, animal subjects, and data from social media platforms were involved in this work. Takehiro Tanaka: Methodology, Software, Data curation, Writing - Original draft, Visualization. Hyomin Choi: Conceptualization, Methodology, Software, Data curation, Supervision, Writing - Review & Editing. Ivan V. Bajic: Conceptualization, Supervision, Writing - Review & Editing, Project administration, Funding acquisition. The authors would like to thank Timothy Woinoski of Simon Fraser University for his help on tracking annotations.




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