Detecting machine-readable codes in industrial manufacturing is a critical yet challenging task, as it directly impacts both defect detection and broader visual anomaly detection. The complexity of visual data, coupled with high-speed production processes generating vast volumes of information, makes accurate and efficient detection essential for ensuring product quality and operational efficiency. This paper takes significant steps toward addressing these challenges by identifying and categorizing key issues related to machine-readable code defect detection, introducing the first publicly available and challenging dataset as a benchmark for future studies, and exploring techniques to enhance both detection performance and data storage efficiency. Experimental results demonstrate that leveraging the YUV color space, combined with data compression, significantly improves detection accuracy while minimizing storage requirements. This work highlights the importance of balancing data complexity, storage optimization, and detection reliability, laying a strong foundation for future advancements in defect detection, anomaly identification, and cost-efficient industrial automation.

Hand Gesture Recognition (HGR) achieved significant progress through diverse fields due to recent advancements in machine learning and sensor technologies. While Leap Motion Controller sensors offer convenient hand tracking and multi-modal data (skeletal and depth), the heterogeneous nature of these data modalities poses several challenges for HGR systems. In order to exploit the complementary information offered by skeleton and depth data, fusion algorithms are widely used. This paper proposes a novel Deep CNN-BiGRU model incorporating both intermediate and late fusion strategies. For each modality, we use a separate model for feature extraction step. Then, we apply fusion techniques for the decision step. Our proposed model demonstrates superior performance compared with models employed separately on skeletal or depth data, highlighting its effectiveness in exploiting the combined information for robust and accurate HGR.

Deploying drones with deep-learning capabilities for real-time object detection is a trendsetting strategy, particularly in dynamic environments such as Intelligent Transportation Systems. Drones, or Unmanned Aerial Vehicles, offer advantages including a wide field of view, cost-effectiveness, and operational efficiency, making them ideal for perception and surveillance in dynamic settings. However, drones face several challenges in terms of on-board computational capabilities, constraining the deployment of complex algorithms for aerial imagery analysis. In this paper, we exhaustively study the challenges of real-time object detection for drones, emphasizing the adaptability and effectiveness of deep learning-based detectors. We fine-tune and evaluate the six most used scaled versions of real-time object detectors for drone-imagery, using a benchmark, tailored for drone-captured data, to assess detection accuracy, inference speed, and computational efficiency. Our study proves that YOLOv5n excels in terms of inference speed at 454 FPS, but with lower precision, while YOLOv8n surpasses in precision but requires more resources and longer inference times. Larger versions show moderate accuracy improvement but demand increased computational resources and extended inference duration.

Person re-identification is still an open challenging task in various fields due to numerous factors, including illumination changes, background clutter, pose state variations and cloth changes. Several approaches have been suggested to address this problem in the context of deep learning. Generative models, particularly Variational Autoencoders (VAEs), have emerged as promising tools to address these challenges by learning discriminative feature representations of individual images. In this paper, we present Soft-Attention based Person Re-Identification (SAPRI), a novel approach that combines VAEs with a supervised ReID method to enhance the resilience and efficacy of ReID systems. The proposed approach focuses on data reconstruction based on soft attention. Variational autoen-coders encode principally person data, while ignoring irrelevant information. By incorporating supervised ReID, the model learns to appropriately classify persons in real world environments. Our SAPRI proposed method has been evaluated on well-known benchmarks, DukeMTMC-reID and CUHK03, demonstrating superior performance compared to existing state-of-the-art techniques in terms of the mean Average Precision evaluation metric (mAP). Additionally, qualitative results show the effectiveness of the VAE in generating discriminative representations of person images.

Person Re-Identification is a critical component in modern video surveillance systems for locating individuals across cameras from various viewpoints. However, one of the significant challenges in person ReID arises when facial information is unavailable. To address this issue, we propose GF2PReID, a novel framework that leverages state-of-the-art deep learning-based ReID approaches to generate prior knowledge of the face region and provide detailed information about human body images. Our approach utilizes a deep residual network model trained with transfer learning to extract discriminative features from images with low discrimination, including low illumination and occlusion, collected from diverse datasets. Experimental results, on 2 challenging datasets: Market-1501 and CUHK03, demonstrate that our GF2PReID framework improves the datasets and significantly improves the performance of the Resnet-50 model, reaching the highest performance.

Driver monitoring at nighttime is a tremendous area of research because of its crucial role to save lives and decrease traffic crashes injuries. However, this task is highly complex because of the high amount of naturalistic driving issues and the low visibility. In the gist of this paper, a novel nighttime driver action recognition network named multi-convolutional stream for hybrid network are proposed, which effectively fuses multimodal data to efficiently classify driver's actions in low visibility and a cluttered driving scene. Using the unique public driver action dataset recorded at nighttime, up to our knowledge, in two separate viewpoints, our proposed methodology beats state-of-the-art methodologies in classification performance, with an advancement of up to 10% over the best-practice methods.

Many challenges are present in the pedestrian detection field which makes it a trending topic. Detecting pedestrian is an extremely difficult task under bad weather conditions. In order to improve and facilitate the detection task, it is required to use infra-red images. For the advanced driver-assistance systems (ADAS), more specifically those of the pedestrian detection, the camera is mounted on a moving vehicle resulting egomotion in the background. Thus another challenging problem is added. It is then required to compensate the background egomotion to obtain a background static scene. In this paper, we introduce an advanced approach for the pedestrian detection under poor weather conditions using a moving camera. First, using the interest point detector Speeded Up Robust Features (SURF), ego-motions in the background are adjusted. After that, the foreground is detected by subtracting frames. Then, a segmentation step is required to divide the images into multiple moving objects. Finally, a recognition process is applied in order to classify the moving objects into both categories: pedestrian and undefined patterns. The proposed approach was evaluated on the CVC14 dataset. Experimental results illustrate the good performance of the approach.