@Article{csse.2023.040410,
AUTHOR = {Safdar Khan, Muhammad Attique Khan, Jamal Hussain Shah, Faheem Shehzad, Taerang Kim, Jae-Hyuk Cha},
TITLE = {Suspicious Activities Recognition in Video Sequences Using DarkNet-NasNet Optimal Deep Features},
JOURNAL = {Computer Systems Science and Engineering},
VOLUME = {47},
YEAR = {2023},
NUMBER = {2},
PAGES = {2337--2360},
URL = {http://www.techscience.com/csse/v47n2/53684},
ISSN = {},
ABSTRACT = {Human Suspicious Activity Recognition (HSAR) is a critical and
active research area in computer vision that relies on artificial intelligence
reasoning. Significant advances have been made in this field recently due
to important applications such as video surveillance. In video surveillance,
humans are monitored through video cameras when doing suspicious activities such as kidnapping, fighting, snatching, and a few more. Although
numerous techniques have been introduced in the literature for routine human
actions (HAR), very few studies are available for HSAR. This study proposes a deep convolutional neural network (CNN) and optimal featuresbased framework for HSAR in video frames. The framework consists of
various stages, including preprocessing video frames, fine-tuning deep models
(Darknet 19 and Nasnet mobile) using transfer learning, serial-based feature
fusion, feature selection via equilibrium feature optimizer, and neural network
classifiers for classification. Fine-tuning two models using some hit and trial
methods is the first challenge of this work that was later employed for feature
extraction. Next, features are fused in a serial approach, and then an improved
optimization method is proposed to select the best features. The proposed
technique was evaluated on two action datasets, Hybrid-KTH01 and HybridKTH02, and achieved an accuracy of 99.8% and 99.7%, respectively. The
proposed method exhibited higher precision compared to existing state-ofthe-art approaches.},
DOI = {10.32604/csse.2023.040410}
}