Human action recognition using a hybrid deep learning heuristic

Dash, Samarendra Chandan Bindu; Mishra, Soumya Ranjan; Raju, K. Srujan; Prasad, L V Narasimha

doi:10.1007/s00500-021-06149-7

Cited by 13 publications

(6 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To demonstrate the performance of the proposed processing units, Π ec , we use the KTH dataset (Schuldt et al, 2004). In this way, we make a coherent comparison between our approach and existing works (Baccouche et al, 2011;Ji et al, 2012;Grushin et al, 2013;Ali and Wang, 2014;Liu et al, 2014;Shu et al, 2014;Shi et al, 2015;Veeriah et al, 2015;Dash et al, 2021). This data set is composed of 6 videos, which involves human actions, such as boxing(a), hand clapping(b), hand waving(c), jogging(e), running (d) and walking(f).…”

Section: Resultsmentioning

confidence: 99%

“…As a consequence, the detection and recognize of the human motion in real-time becomes infeasible since the latency of the RPM system is increased significantly. In the past decades, several approaches have been developed to efficiently perform human action recognition (Baccouche et al, 2011;Ji et al, 2012;Grushin et al, 2013;Ali and Wang, 2014;Liu et al, 2014;Shu et al, 2014;Shi et al, 2015;Veeriah et al, 2015;Dash et al, 2021). In general terms, these approaches intend to determine when the action is occurring and what is this action by considering the starting and ending times of all action occurrences from the video.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A biologically inspired spiking neural P system in selective visual attention for efficient feature extraction from human motion

et al. 2022

View full text Add to dashboard Cite

Nowadays, human action recognition has become an essential task in health care and other fields. During the last decade, several authors have developed algorithms for human activity detection and recognition by exploiting at the maximum the high-performance computing devices to improve the quality and efficiency of their results. However, in real-time and practical human action recognition applications, the simulation of these algorithms exceed the capacity of current computer systems by considering several factors, such as camera movement, complex scene and occlusion. One potential solution to decrease the computational complexity in the human action detection and recognition can be found in the nature of the human visual perception. Specifically, this process is called selective visual attention. Inspired by this neural phenomena, we propose for the first time a spiking neural P system for efficient feature extraction from human motion. Specifically, we propose this neural structure to carry out a pre-processing stage since many studies have revealed that an analysis of visual information of the human brain proceeds in a sequence of operations, in which each one is applied to a specific location or locations. In this way, this specialized processing have allowed to focus the recognition of the objects in a simpler manner. To create a compact and high speed spiking neural P system, we use their cutting-edge variants, such as rules on the synapses, communication on request and astrocyte-like control. Our results have demonstrated that the use of the proposed neural P system increases significantly the performance of low-computational complexity neural classifiers up to more 97% in the human action recognition.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A biologically inspired spiking neural P system in selective visual attention for efficient feature extraction from human motion

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Of 96.75% [ 172 ] 2021 Automatically learned features a deep bottleneck multimodal feature fusion (D-BMFF) framework that fused three different modalities of RGB, RGB-D(depth) and 3D coordinates information for activity classification Four RGB-D datasets: UT Kinect, CAD-60, Florence 3D, and SBU Interaction The ARR on UT Kinect, CAD-60, Florence 3D, SBU Interaction are 99%, 98.50%, 98.10%, and 97.75% respectively [ 209 ] 2021 Automatically learned features approach for human activity recognition using ensemble learning of multiple convolutional neural network (CNN) models Ensemble of CNN models gives accuracy of 94% [ 26 ] 2021 Automatically learned features deep learning-based method for human activity recognition problem. The method uses convolutional neural networks to automatically extract features from raw sensor data and classify six basic human activities Diabetes dataset Different activities covered with approximately 90% accuracy using CNN,Random forest, SVM [ 135 ] 2021 Automatically learned features a deep learning architecture that leverages the feature extraction capability of the convolutional neural networks and the construction of the temporal sequences of recurrent neural networks to improve existing classification results Accuracy increased from 30% to 35% due to transfer learning [ 57 ] 2021 Automatically learned features, handcrafted features framework to extract handcrafted high-level motion features and in-depth features by CNN in parallel to recognize human action. SIFT is used as a handcrafted feature to encode high-level motion features from the maximum number of input video frames.…”

Section: Approaches Of Harmentioning

confidence: 99%

A review of vision-based indoor HAR: state-of-the-art, challenges, and future prospects

Bhola

Vishwakarma

2023

Multimed Tools Appl

View full text Add to dashboard Cite

With the advent of technology, we are getting more comfortable with the use of gadgets, cameras, etc., and find Artificial Intelligence as an integral part of most of the tasks we perform throughout the day. In such a scenario, the use of cameras and vision-based sensors comes as an escape from many real-time problems and challenges. One major application of these vision-based systems is Indoor Human Activity Recognition (HAR) which serves in a variety of scenarios ranging from smart homes, elderly care, assisted living, and human behavior pattern analysis for identifying any abnormal behavior to abnormal activity recognition like falling, slipping, domestic violence, etc. The effect of HAR in real time has made the area of indoor activity recognition a more explored zone by the industrial segment to attract users with their products in multiple domains. Hence, considering these aspects of HAR, this work proposes a detailed survey on indoor HAR. Through this work, we have highlighted the recent methodologies and their performance in the field of indoor activity recognition. We have also discussed- the challenges, detailed study of approaches with real-world applications of indoor-HAR, datasets available for indoor activity, and their technical details in this work. We have proposed a taxonomy for indoor HAR and highlighted the state-of-the-art and future prospects by mentioning the research gaps and the shortcomings of recent surveys with respect to our work.

show abstract

“…Moreover, the major limitation of moving joint descriptor is obstinacy and lesser accuracy [39]. Similarly, a novel approach was designed in [40] that extracts high level movement features by scale invariant feature transform and deep features to classify the activities [40]. However, there were some errors in the corresponding feature points attained by this method, and because of this, the accuracy was gradually decreased [41].…”

Section: Literature Reviewmentioning

confidence: 99%

An Efficient Feature Selection Method for Video-Based Activity Recognition Systems

Ṣiddiqi

Alsirhani

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Human activity recognition (HAR) is the examination of gestures and actions of humans from various resources such as depth or RGB cameras. In this work, we have designed a dynamic and robust feature selection algorithm for a HAR system, through which the system accurately recognizes various kinds of activities. In the proposed approach, we employed mutual information algorithm, which selects the prominent features from the extracted features. The proposed algorithm is the expansion of two methods like max-relevance and min-redundancy, respectively. This method has the capability to gather the assets of various extraction algorithms. But the procedure of selection may be unfair due to the dissimilarity between the classification power and redundancy of the features. To resolve this type of unfair selection, we stabilize both parts through the proposed algorithm that has autonomous upper limit of the mutual information function. Likewise, for the feature extraction and recognition, we used the symlet wavelet transform and hidden Markov model, respectively, for action classification. The proposed algorithm has been justified on depth-based database which has thirteen kinds of activities under comprehensive set of experiments. We showed that the proposed feature selection method achieved best classification accuracy against existing works.

show abstract

Human action recognition using a hybrid deep learning heuristic

Cited by 13 publications

References 45 publications

A biologically inspired spiking neural P system in selective visual attention for efficient feature extraction from human motion

A biologically inspired spiking neural P system in selective visual attention for efficient feature extraction from human motion

A review of vision-based indoor HAR: state-of-the-art, challenges, and future prospects

An Efficient Feature Selection Method for Video-Based Activity Recognition Systems

Contact Info

Product

Resources

About