Human Action Detection and Recognition Using SIFT and SVM

Dhulavvagol, Praveen M.; Kundur, Niranjan C.

doi:10.1007/978-981-10-9059-2_42

Cited by 10 publications

(2 citation statements)

References 10 publications

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“…Dhulavvagol P.M., Kundur N.C. [7] proposed a combination of two different techniques i.e. SVM and SIFT techniques to identify and recognize the human actions in a given video or image.…”

Section: Existing Related Workmentioning

confidence: 99%

Video Based Human Activity Detection, Recognition and Classification of actions using SVM

Basavaiah

Patil²

2018

TMLAI

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Human motion analysis which includes activity detection and action recognition is currently gaining attention from computer vision researchers. Automatic monitoring of human activities and actions using computers has found significant applications in video surveillance, monitoring of patients and sports applications. With the tremendous advancement and development in digital video libraries, automatic interpretation of videos will save human effort in analysis and interpretation. This has led to the development of robust techniques in the field of computer vision. Human activity detection and recognition includes detection of human, tracking of human and recognition of actions. In this paper, detection of human is done using Gaussian Mixture Model, tracking is done using optical flow, recognition and classification of actions is done using SVM Classifier. The experiment is carried out with two public datasets KTH and Weizmann which are the videos with constant background. The classification accuracy for KTH dataset is 92.48% and for Weizmann dataset the classification accuracy is 93.64%.

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“…Dhulavvagol P.M., Kundur N.C. [7] proposed a combination of two different techniques i.e. SVM and SIFT techniques to identify and recognize the human actions in a given video or image.…”

Section: Existing Related Workmentioning

confidence: 99%

Video Based Human Activity Detection, Recognition and Classification of actions using SVM

Basavaiah

Patil²

2018

TMLAI

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“…Other studies employed purely HOG features, e.g. Thurau and Hlavac [3], Desai et al [4], Maji et al [8], Desai and Ramanan [9], Delaitre et al [10], and Yao et al [13], while SIFT features were used purely in Li and Fei-Fei [6], Sharma et al [19], and Dhulavvagol and Kundur [20].…”

Section: Introductionmentioning

confidence: 99%

An evolving ensemble model of multi-stream convolutional neural networks for human action recognition in still images

Slade

Zhang

et al. 2022

Neural Comput & Applic

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Still image human action recognition (HAR) is a challenging problem owing to limited sources of information and large intra-class and small inter-class variations which requires highly discriminative features. Transfer learning offers the necessary capabilities in producing such features by preserving prior knowledge while learning new representations. However, optimally identifying dynamic numbers of re-trainable layers in the transfer learning process poses a challenge. In this study, we aim to automate the process of optimal configuration identification. Specifically, we propose a novel particle swarm optimisation (PSO) variant, denoted as EnvPSO, for optimal hyper-parameter selection in the transfer learning process with respect to HAR tasks with still images. It incorporates Gaussian fitness surface prediction and exponential search coefficients to overcome stagnation. It optimises the learning rate, batch size, and number of re-trained layers of a pre-trained convolutional neural network (CNN). To overcome bias of single optimised networks, an ensemble model with three optimised CNN streams is introduced. The first and second streams employ raw images and segmentation masks yielded by mask R-CNN as inputs, while the third stream fuses a pair of networks with raw image and saliency maps as inputs, respectively. The final prediction results are obtained by computing the average of class predictions from all three streams. By leveraging differences between learned representations within optimised streams, our ensemble model outperforms counterparts devised by PSO and other state-of-the-art methods for HAR. In addition, evaluated using diverse artificial landscape functions, EnvPSO performs better than other search methods with statistically significant difference in performance.

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Performance Analysis of Job Scheduling Algorithms on Hadoop Multi-cluster Environment

Dhulavvagol

Totad

Sourabh

2019

Lecture Notes in Electrical Engineering

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Human Action Detection and Recognition Using SIFT and SVM

Cited by 10 publications

References 10 publications

Video Based Human Activity Detection, Recognition and Classification of actions using SVM

Video Based Human Activity Detection, Recognition and Classification of actions using SVM

An evolving ensemble model of multi-stream convolutional neural networks for human action recognition in still images

Performance Analysis of Job Scheduling Algorithms on Hadoop Multi-cluster Environment

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