Besides, SOTVerse provides two mechanisms with new indicators and successfully evaluates trackers under various subtasks. Specifically, SOTVerse automatically labels challenging factors per frame, allowing users to generate user-defined spaces efficiently via construction rules. Then, we summarize task characteristics, clarify the organization standards, and construct SOTVerse with 12.56 million frames. We first propose a 3E Paradigm to describe tasks by three components (i.e., environment, evaluation, and executor). In this article, we systematize the representative benchmarks and form a single object tracking metaverse (SOTVerse) - a user-defined SOT task space to break through the bottleneck. The former causes existing datasets can not be exploited comprehensively, while the latter neglects challenging factors in the evaluation process. However, isolated experimental environments and limited evaluation methods more seriously hinder the SOT research.
Single object tracking (SOT) research falls into a cycle - trackers perform well on most benchmarks but quickly fail in challenging scenarios, causing researchers to doubt the insufficient data content and take more effort constructing larger datasets with more challenging situations. The dataset, the evaluation kit and the results are publicly available at the challenge website ().
#Uiuc timetracker max weekly hours for part time code#
The source code for most of the trackers is publicly available from the VOT page. Performance of the tested trackers typically by far exceeds standard baselines. The VOT toolkit has been updated to support both standard short-term and the new long-term tracking subchallenges. A new dataset has been compiled and a performance evaluation methodology that focuses on long-term tracking capabilities has been adopted. The new subchallenge focuses on long-term tracking properties, namely coping with target disappearance and reappearance. A long-term tracking subchallenge has been introduced to the set of standard VOT sub-challenges.
The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis and a “real-time” experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. Results of over eighty trackers are presented many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative.
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