Suspicious and Violent Activity Detection of Humans Using HOG Features and SVM Classifier in Surveillance Videos

In this paper a novel voice activity detection approach using smoothed fuzzy entropy (smFuzzyEn) feature using support vector machine is proposed. The proposed approach (smFESVM) uses total variation filter and Savitzky-Golay filter to smooth the FuzzyEn feature extracted from the noisy speech signals. Also, convolution of the first order difference of TV filter and noisy fuzzy entropy feature (conFETV') is also proposed. The obtained smoothed feature vectors are further normalized using min-max normalization and the normalized feature vectors train SVM model for speech/non-speech classification. The proposed smFESVM method shows better discrimination of noise and noisy speech when tested under various nonstationary background noises of different signal-to-noise ratio levels. 10 – fold cross validation was used to validate the efficacy of the SVM classifier. The performance of the smFESVM is compared against various algorithms and comparison suggests that the results obtained by the smFESVM is efficient in detecting speech under low SNR conditions with an accuracy of 93.88%.

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Separable Convolution Neural Network for Abnormal Activity Detection in Surveillance Videos

Innovative Data Communication Technologies and Application - Lecture Notes on Data Engineering and Communications Technologies ◽

10.1007/978-981-15-9651-3_29 ◽

2021 ◽

pp. 331-346

Author(s):

S. S. Anju ◽

K. V. Kavitha

Keyword(s):

Neural Network ◽

Convolution Neural Network ◽

Activity Detection ◽

Surveillance Videos ◽

Abnormal Activity Detection

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Activity Normalization for Activity Detection in Surveillance Videos

2020 IEEE International Conference on Image Processing (ICIP) ◽

10.1109/icip40778.2020.9190884 ◽

2020 ◽

Author(s):

Takashi Hosono ◽

Kiyohito Sawada ◽

Yongqing Sun ◽

Kazuya Hayase ◽

Jun Shimamura

Keyword(s):

Activity Detection ◽

Surveillance Videos

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Machine Learning Verdict of EEG Signals in Brain Computer Interface

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit1838114 ◽

2018 ◽

pp. 429-441

Author(s):

M. Jeyanthi ◽

C. Velayutham

Keyword(s):

Nearest Neighbor ◽

Technology Development ◽

Vital Role ◽

Svm Classifier ◽

K Nearest Neighbor ◽

Data Mining Technique ◽

Data Set ◽

Eeg Data ◽

Irrelevant Attributes

In Science and Technology Development BCI plays a vital role in the field of Research. Classification is a data mining technique used to predict group membership for data instances. Analyses of BCI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to raw data. In this paper, We extracted features using statistical Haralick features from the raw EEG data . Then the features are Normalized, Binning is used to improve the accuracy of the predictive models by reducing noise and eliminate some irrelevant attributes and then the classification is performed using different classification techniques such as Naïve Bayes, k-nearest neighbor classifier, SVM classifier using BCI dataset. Finally we propose the SVM classification algorithm for the BCI data set.

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