# Digital Image Processing: Advanced Technologies and Applications
## 2 An Overview of Published Articles
In recognition of the importance of the video classification task and to summarize the success of deep learning models, contribution six is a concise review of the said topic. Particularly, this work highlights several major findings that are based on existing deep learning algorithms. This review emphasizes the type of architectures used, the evaluation criteria, and the experimented datasets. Moreover, a fair insight into the recently reported deep learning methods and traditional approaches is also provided. Furthermore, the important tasks based on the targets are highlighted to calculate the technical advancement of these systems. In the seventh contribution, researchers addressed the task of multiple-object tracking (MOT) in complex scenarios, such as instances of missed detections, false alarms, and frequent target switching. This contribution has explicit potential applications in security applications, which include public safety and fire prevention, to track crucial targets. Therefore, researchers proposed an approach to multi-object tracking and an identity validity discrimination module. The authors raised the KC-YOLO detection model for tracking, optimized detection frames, and implemented adaptive feature refinement to solve challenges, for instance, incomplete pedestrian features, which are caused by occlusion. The method proposed in this work improves pedestrian tracking accuracy along with pedestrian characteristics. In experiments on the MOT16, MOT17, and MOT20 datasets, this method resulted in substantial findings and encouraging results. The eighth contribution in this Special Issue is related to the study of recognizing handwritten Arabic characters. Given the fundamental complexities of the Arabic characters that encompass semi-cursive styles, apparent character models, and the insertion of diacritical spots, this area of research has great potential. Highlights in this work are on children's handwritten Arabic writing. This area is recognized for its apparent challenges, for example, variations in writing and distortions. The researchers also collected a dataset, referred to as \"Dhad\". Their investigation employs a tri-fold experimental approach, covering the investigation of pre-trained deep learning algorithms, custom-designed ConvNets architectures, and established classifiers. These findings sort out the efficiency of fine-tuned models, the potential of custom ConvNets designs, and the details associated with several classification paradigms. The pre-trained model yields the best test accuracy, at 93.59%, with the authors' collected dataset. Moreover, researchers also proposed the idea of a novel application specifically for children younger than 13, with the aim of improving their handwriting skills. The ninth contribution in this Special Issue is related to the analysis of mammography images using multi-branch attentional ConvNets. In this work, a research team proposed a method based on the multi-label classification of two-view mammography images. It influences the correlation between lesion type and its different states. It then classifies mammograms into density, anomaly type, and difficulty level. It takes two-view mammograms as input, analyzes them using ConvNeXt and the channel attention mechanism, and integrates this information. Finally, the combined information is fed into multi-branches, which learn pattern representations to predict the appropriate state. This algorithm was evaluated on two public domain benchmark datasets, INBreast and the Curated Breast Imaging Subset of DDSM. The developed CAD method discusses the holistic performance of a patient's state. It guides radiologists in the analysis of mammograms with a facility to prepare a complete report of a patient's condition with high confidence. The tenth contribution in this Special Issue is about the detection and classification of vehicles from publicly available datasets through YOLO-v5. The authors use a transfer learning method on the packed traffic patterns.