Global Contrast Based Salient Region Boundary Sampling for Action Recognition

Xu, Zengmin; Hu, Ruimin; Chen, Huafeng; Li, Hongyang

doi:10.1007/978-3-319-27671-7_16

Cited by 8 publications

(3 citation statements)

References 27 publications

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“…The UCF50 dataset 12 (as shown in Figure 3C) is collected from real‐world videos of YouTube and has 50 action categories. The action clips range from common sports to daily living activities 31 . The action videos are divided into 25 groups in every class.…”

Section: Methodsmentioning

confidence: 99%

Real‐time action feature extraction via fast PCA‐Flow

Chen

et al. 2019

Concurrency and Computation

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Summary Action recognition is a research hotspot in the field of Internet of Things (IoT). Currently, local pixel‐domain spatiotemporal feature extraction methods have reached the state‐of‐the‐art action recognition performance on many challenging datasets. However, the poor computational complexity of these approaches prevents them from scaling up to real‐time applications. For solving this problem, we present a novel real‐time video feature extraction technique by exploiting the fast PCA‐Flow algorithm. Firstly, we down‐sample video images in form of grid. Based on the down‐sampling images, PCA‐Flow algorithm is used to calculate optical flow among adjacent images. The PCA‐Flow matrices are then expanded to the original video image size by using efficient gCLSR super‐resolution method to keep the inherent geometric structure of the optical flow. Finally, we compute action descriptors based on original pixel frames and the enlarged PCA‐Flow images. The proposed approach is validated on three challenging datasets: UCF50, Hollywood2, and HMDB51. Experimental results indicate that the proposed method is more efficient in computation and can achieve competitive quality than the state‐of‐the‐art methods.

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Section: Methodsmentioning

confidence: 99%

Real‐time action feature extraction via fast PCA‐Flow

Chen

et al. 2019

Concurrency and Computation

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“…Effective feature representation is key to image processing [1][2][3] and video understanding [4][5][6]. Spatio-temporal features [7,8], subspace features [9,10], and label information [11,12] have been investigated for action recognition.…”

Section: Introductionmentioning

confidence: 99%

Action Recognition via Adaptive Semi-Supervised Feature Analysis

et al. 2023

Applied Sciences

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This study presents a new semi-supervised action recognition method via adaptive feature analysis. We assume that action videos can be regarded as data points in embedding manifold subspace, and their matching problem can be quantified through a specific Grassmannian kernel function while integrating feature correlation exploration and data similarity measurement into a joint framework. By maximizing the intra-class compactness based on labeled data, our algorithm can learn multiple features and leverage unlabeled data to enhance recognition. We introduce the Grassmannian kernels and the Projected Barzilai–Borwein (PBB) method to train a subspace projection matrix as a classifier. Experiment results show our method has outperformed the compared approaches when a few labeled training samples are available.

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“…This can directly affect large datasets in term of video duration, resolution and also number of classes. Moreover, for the aforementioned reason, IDT is expensive in terms of computation and computer data storage [179].…”

Section: Introductionmentioning

confidence: 99%

Action analysis and video summarisation to efficiently manage and interpret video data

Carvajal¹

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In the last few years we have seen how the volume of video data has exponentially grown. Specialised online sites like YouTube and NetFlix are attracting a considerable amount of audience who are uploading, accessing, and actively interacting with the online sites. Furthermore, millions of video surveillance cameras have been installed around the world. Video cameras are installed to monitor shopping centres, universities, parks, streets, and in general to monitor any public place.

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Global Contrast Based Salient Region Boundary Sampling for Action Recognition

Cited by 8 publications

References 27 publications

Real‐time action feature extraction via fast PCA‐Flow

Real‐time action feature extraction via fast PCA‐Flow

Action Recognition via Adaptive Semi-Supervised Feature Analysis

Action analysis and video summarisation to efficiently manage and interpret video data

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