Search

The fast and nondestructive identification of mobile phone cases is a crucial task for law enforcement personnel in the field of forensic science. Attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) has been employed in conjunction with feature extraction and deep learning algorithms to develop a method for distinguishing mobile phone cases. As preprocessing steps, multivariate scatter correction and standard normal variate were performed. Feature variables were extracted using the competitive adaptive reweighted sampling and shuffled frog leaping algorithm. Several supervised pattern recognition methods, namely ResNet50, VGG16, and multilayer perceptron (MLP), were utilized for constructing classifiers. The results indicated that the model's performance based on multivariate scattering correction (MSC) with competitive adaptive re-weighted sampling (CARS) surpassed that of other approaches. Notably, the ResNet50 model exhibited superior discriminative ability compared to the VGG16 and MLP models, achieving 100% accuracy in the differentiation of all samples. This designed method represents a potentially rapid and nondestructive approach for identifying mobile phone cases in forensic science. [ABSTRACT FROM AUTHOR]

With the proliferation of urban video surveillance systems, the abundance of surveillance video data has emerged as a pivotal asset for enhancing public safety. Within these video archives, the identification of abnormal human actions carries profound implications for security incidents. Nevertheless, existing surveillance systems primarily rely on conventional algorithms, leading to both missed incidents and false alarms. To address the challenge of automating multi-object surveillance video analysis, this study introduces a comprehensive method for the detection and recognition of multi-object abnormal actions. This study comprises a two-stage framework: the coarse detection stage employs an enhanced YOWOv2E model for spatio-temporal action detection, while the precise detection stage utilizes a two-stream network for precise action classification. In parallel, this paper presents the PSA-Dataset to address the current limitations in the field of abnormal action detection. Experimental results, collected from both public datasets and a self-built dataset, illustrate the effectiveness of the proposed method in identifying a wide spectrum of abnormal actions. This work offers valuable insights for automating the analysis of human actions in videos pertaining to public security. [ABSTRACT FROM AUTHOR]