During the performance optimization of a computervision system, developers frequently run into platform-level inefficiencies and bottlenecks that can not be addressed by traditional methods. OpenVX is designed to add...
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ISBN:
(纸本)9781479943098
During the performance optimization of a computervision system, developers frequently run into platform-level inefficiencies and bottlenecks that can not be addressed by traditional methods. OpenVX is designed to address such system-level issues by means of a graph-based computation model. This approach differs from the traditional acceleration of one-off functions, and exposes optimization possibilities that might not be available or obvious with traditional computervision libraries such as OpenCV.
We present a vision-based method for signer diarization - the task of automatically determining "who signed when?" in a video. This task has similar motivations and applications as speaker diarization but ha...
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ISBN:
(纸本)9780769549903
We present a vision-based method for signer diarization - the task of automatically determining "who signed when?" in a video. This task has similar motivations and applications as speaker diarization but has received little attention in the literature. In this paper, we motivate the problem and propose a method for solving it. The method is based on the hypothesis that signers make more movements than their interlocutors. Experiments on four videos (a total of 1.4 hours and each consisting of two signers) show the applicability of the method. The best diarization error rate (DER) obtained is 0.16.
We investigate the problem of recognizing words from video, fingerspelled using the British Sign Language (BSL) fingerspelling alphabet. This is a challenging task since the BSL alphabet involves both hands occluding ...
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ISBN:
(纸本)9781424439942
We investigate the problem of recognizing words from video, fingerspelled using the British Sign Language (BSL) fingerspelling alphabet. This is a challenging task since the BSL alphabet involves both hands occluding each other and contains signs which are ambiguous from the observer's viewpoint. The main contributions of our work include: (i) recognition based on hand shape alone, not requiring motion cues;(ii) robust visual features for hand shape recognition;(iii) scalability to large lexicon recognition with no re-training. We report results on a dataset of 1,000 low quality web-cam videos of 100 words. The proposed method achieves a word recognition accuracy of 98.9%.
Action recognition is one of the major challenges of computervision. Several approaches have been proposed using different descriptors and multi-class models. In this paper, we focus on binary ranking models for the ...
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ISBN:
(纸本)9780769549903
Action recognition is one of the major challenges of computervision. Several approaches have been proposed using different descriptors and multi-class models. In this paper, we focus on binary ranking models for the action recognition problem and address the action recognition as a ranking problem. A binary ranking model is trained for each action and used to recognize the test videos for that action. Binary ranking models are constructed using dense SIFT (DSIFT) descriptors and histogram of oriented gradients / histogram of optical flows (HOG/HOF) descriptors. We show that using ranking models, it is possible to obtain higher recognition accuracies from a baseline that is based on multi-class models on the very recent and challenging benchmark datasets;Human Motion Database (HMDB) and The Action Similarity Labeling (ASLAN).
Material recognition is researched in both computervision and vision science fields. In this paper, we investigated how humans observe material images and found the eye fixation information improves the performance o...
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ISBN:
(数字)9781538661000
ISBN:
(纸本)9781538661000
Material recognition is researched in both computervision and vision science fields. In this paper, we investigated how humans observe material images and found the eye fixation information improves the performance of material image classification models. We first collected eye-tracking data from human observers and used it to fine-tune a generative adversarial network for saliency prediction (SalGAN). We then fused the predicted saliency map with material images and fed them to CNN models for material classification. The experiment results show that the classification accuracy is improved than those using original images. This indicates that human's visual cues could benefit computational models as priors.
We develop a deep convolutional neural networks (CNNs) to deal with the blurry artifacts caused by the defocus of the camera using dual-pixel images. Specifically, we develop a double attention network which consists ...
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ISBN:
(纸本)9781665448994
We develop a deep convolutional neural networks (CNNs) to deal with the blurry artifacts caused by the defocus of the camera using dual-pixel images. Specifically, we develop a double attention network which consists of attentional encoders, triple locals and global local modules to effectively extract useful information from each image in the dual-pixels and select the useful information from each image and synthesize the final output image. We demonstrate the effectiveness of the proposed deblurring algorithm in terms of both qualitative and quantitative aspects by evaluating on the test set in the NTIRE 2021 Defocus Deblurring using Dual-pixel Images Challenge [1] [4].
In this paper we present our approach to the Track 1 of the 2021 AI City Challenge. The goal of the challenge track is to to analyse footage captured with traffic cameras by counting the number of vehicles performing ...
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ISBN:
(纸本)9781665448994
In this paper we present our approach to the Track 1 of the 2021 AI City Challenge. The goal of the challenge track is to to analyse footage captured with traffic cameras by counting the number of vehicles performing various pre-defined motions of interest. Our approach is based on the CenterTrack object detection and tracking neural network used in conjunction with a simple IoU-based tracking algorithm. In the public evaluation server our system achieved the S1 score of 0.8449 placing it at the 8th place on the public leaderboard.
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