A New Approach for Video Action Recognition: CSP-Based Filtering for Video to Image Transformation

Rodríguez-Moreno, Itsaso; Martínez-Otzeta, José María; Goienetxea, Izaro; Rodriguez, Igor; Sierra, Basilio

doi:10.1109/access.2021.3118829

Cited by 6 publications

(3 citation statements)

References 55 publications

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“…Recognition method Accuracy in % 1*Common Spatial Patterns [19] CNN-LSTM 98.69 1*Gray level intensity images [23] 3-D CNN + ConvLSTM 99.00 1*Body skeletal data [10] LSTM network with meta-learner 98.09 1*Gray level intensity images [14] 3 6. an accuracy of 97.70% obtained with the same model for ISL for emergency situations dataset.…”

Section: Feature Extraction Methodsmentioning

confidence: 99%

Sign Language Gesture Recognition from Spatio-temporal Features

Rajput

Purushothaman

Jeena

et al. 2022

Preprint

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Sign Language is the primary mode of communication for the hearing impaired community and between the community and the outside world. This paper proposes a vision-based sign language gesture recognition model to identify the sign gesture (word) from a hand gesture video. The proposed model consists of three modules: Pre-processing, Convolutional Neural Network, Recurrent Neural Network. Pre-processing module is used to extract the frames, segment the region of interest, and convert them into a grayscale image. Convolutional Neural Network is used to extract the spatial features for each frame and each video is represented by a sequence of spatial features. Recurrent Neural Network recognizes the gestures based on spatio-temporal relation in the sequence of features. The evaluation of the proposed model is done on two different datasets. With Argentinian Sign Language (LSA 64), the model is able to achieve an accuracy of 100%. The model achieved an accuracy of 97.70% with the data set: Indian Sign Language (ISL) for Emergency Situations.

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Section: Feature Extraction Methodsmentioning

confidence: 99%

Sign Language Gesture Recognition from Spatio-temporal Features

Rajput

Purushothaman

Jeena

et al. 2022

Preprint

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“…Other research has centered on the detection of human posture to subsequently classify gestures or expressions. In this line, the studies conducted in [ 26 ] present the novel design of a pipeline for the recognition of actions in videos recorded by a humanoid robot, which would give robots action recognition capacities to enhance social interaction. With this purpose, a sequence was created that employs a Common Spatial Patterns (CSP) algorithm that processes signals obtained from skeleton joints of the person performing the action on the video.…”

Section: Related Workmentioning

confidence: 99%

Bioinspired Photoreceptors with Neural Network for Recognition and Classification of Sign Language Gesture

Urrea,

Kern,

Navarrete

2023

Sensors

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This work addresses the design and implementation of a novel PhotoBiological Filter Classifier (PhBFC) to improve the accuracy of a static sign language translation system. The captured images are preprocessed by a contrast enhancement algorithm inspired by the capacity of retinal photoreceptor cells from mammals, which are responsible for capturing light and transforming it into electric signals that the brain can interpret as images. This sign translation system not only supports the effective communication between an agent and an operator but also between a community with hearing disabilities and other people. Additionally, this technology could be integrated into diverse devices and applications, further broadening its scope, and extending its benefits for the community in general. The bioinspired photoreceptor model is evaluated under different conditions. To validate the advantages of applying photoreceptors cells, 100 tests were conducted per letter to be recognized, on three different models (V1, V2, and V3), obtaining an average of 91.1% of accuracy on V3, compared to 63.4% obtained on V1, and an average of 55.5 Frames Per Second (FPS) in each letter classification iteration for V1, V2, and V3, demonstrating that the use of photoreceptor cells does not affect the processing time while also improving the accuracy. The great application potential of this system is underscored, as it can be employed, for example, in Deep Learning (DL) for pattern recognition or agent decision-making trained by reinforcement learning, etc.

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“…Which utilized to identify and track the positions of 33 skeleton points of body joint landmarks from RGB inputs under the real-time proceeding speed. Recently, many researchers utilized this tool for their active research [8][9][10]. The pipeline initially locates the region-of-interest (ROI) inside of the frame using a detector.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Human Pose Estimation and Processing With the Intensive Feature Consistency Network

et al. 2023

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The modeling of human body kye-points is the most significant aspect of pose estimation appropriately. Computer vision algorithm identifies human pose, body-movement, and action in many ways. Most of the previous works taken advantage for finding accuracy or efficiency in terms of speed. However, many techniques suffer for intensive computational demands with low-latency or higher proceeding speed. We have designed a unique approach for single-person pose estimation and action recognition which is well suited for fitness application and mobility activities. The proposed framework has been developed with a base network that provides an initial pose to further refinement through Intensive Feature Consistency (IFC) network. The IFC network enforces high-level constraints on the global body intensity correction and local body part adjustments. The proposed module reduces the impact of body joint movement diversity by interpreting long-term consistent view. We have illustrated the effectiveness of proposed framework through pose estimation accuracy improvement with two benchmark datasets. Which is specified state-of the-art performance of IFC network under the required real-time processing speed on the CPU platform. The IFC network has improved 99.1% of PCK body and 94.7% of PCK torso accuracy under 31 FPS, which is comparatively higher than the existing work.INDEX TERMS Single person pose estimation, intensive feature consistency, global body intensity, local part adjustments, skeleton joint key-points.

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A New Approach for Video Action Recognition: CSP-Based Filtering for Video to Image Transformation

Cited by 6 publications

References 55 publications

Sign Language Gesture Recognition from Spatio-temporal Features

Sign Language Gesture Recognition from Spatio-temporal Features

Bioinspired Photoreceptors with Neural Network for Recognition and Classification of Sign Language Gesture

Improvement of Human Pose Estimation and Processing With the Intensive Feature Consistency Network

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