This paper presents a solution for Track 1 of the AI City Challenge 2023, which involves Multi-Camera People Tracking in indoor scenarios. The proposed framework comprises four modules: Vehicle detection, ReID feature...
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Carbon nanotube (CNT) forests are imaged using scanning electron microscopes (SEMs) that project their multilayered 3D structure into a single 2D image. Image analytics, particularly instance segmentation is needed to...
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Although there are many methods based on deep learning that have superior performance on single image super-resolution (SISR), it is difficult to run in real time on devices with limited computing power. Some recent s...
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In this paper, we release the Simulated Articulated VEhicles Dataset (SAVED) which contains images of synthetic vehicles with moveable vehicle parts. SAVED consists of images that are much more relevant for vehicle-re...
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ISBN:
(纸本)9781665458245
In this paper, we release the Simulated Articulated VEhicles Dataset (SAVED) which contains images of synthetic vehicles with moveable vehicle parts. SAVED consists of images that are much more relevant for vehicle-related pattern-recognition tasks than other popular pretraining datasets such as ImageNet. Compared to a model initialized with ImageNet weights, we show that a model pretrained using SAVED leads to much better performance when recognizing vehicle parts and orientation directly from an image. We also find that a multi-task pretraining approach using fine-grained geometric signals available in SAVED leads to significant improvements in performance. By pretraining on SAVED instead of ImageNet, we reduce the error rate of one of the state of the art vehicle orientation estimators by 51.2% when tested on real images. We release SAVED and instructions on its usage here(1).
Current video/action understanding systems have demonstrated impressive performance on large recognition tasks. However, they might be limiting themselves to learning to recognize spatiotemporal patterns, rather than ...
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ISBN:
(纸本)9781665458245
Current video/action understanding systems have demonstrated impressive performance on large recognition tasks. However, they might be limiting themselves to learning to recognize spatiotemporal patterns, rather than attempting to thoroughly understand the actions. To spur progress in the direction of a more comprehensive understanding of videos, we introduce the task of win-fail action recognition - differentiating between successful and failed attempts at various activities. We introduce a first of its kind paired win-fail action understanding dataset with samples from the following domains: "General Stunts," "Internet Wins-Fails," "Trick Shots," & "Party Games." Unlike existing action recognition datasets, intra-class variation is high making the task challenging, yet feasible. Using a battery of experiments, including a novel video retrieval test, we systematically analyze the characteristics of our win-fail task/dataset, and determine its suitability to serve as a video understanding problem benchmark. While current prototypical action recognition methods work well on our task/dataset, they still leave a large gap to achieve high performance. We hope to motivate more work towards the true understanding of actions/videos. Dataset will be available from: https://***/ParitoshParmar/Win-Fail-Action-recognition.
The increasing use of deep learning models in critical areas of computervision and the consequent need for insights into model behaviour have led to the development of numerous feature attribution methods. However, t...
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Recently, the task of contactless physiological signal monitoring based on deep learning technologies has attracted a large number of scholars. However, few studies focus on the application of real-world scenarios, es...
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We propose a post-processor, called NeighborTrack, that leverages neighbor information of the tracking target to validate and improve single-object tracking (SOT) results. It requires no additional data or retraining....
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The use of helmets is essential for motorcyclists' safety, but non-compliance with helmet rules remains a common issue. In this study, we extend the frontier of AI video analytic technologies for detecting violati...
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Computational topology has consequently shorten the time taken for image recognition with good accuracy and therefore has boosted the performance of computervision. This paper uses computational topology in different...
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