In maritime surveillance systems, object detection plays a crucial role in ensuring the security of nearby waters by tracking the movement of various objects, such as ships and aircrafts, that are found at sea, detecting illegal activities and preemptively countering or predicting potential risks. Using vision sensors such as cameras to monitor the sea can help to identify the shape, size, and color of objects, enabling the precise analysis of maritime situations. Additionally, vision sensors can monitor or track small ships that may escape radar detection. However, objects located at considerable distances from vision sensors have low resolution and are small in size, rendering their detection difficult. This paper proposes a multifocal object detection associative network (MODAN) to overcome these vulnerabilities and provide stable maritime surveillance. First, it searches for the horizon using color quantization based on K-means; then, it selects and partitions the region of interest (ROI) around the horizon using the ROI selector. The original image and ROI image, converted to high resolution through the Super-Resolution Convolutional Neural Network (SRCNN), are then passed to the near-field and far-field detectors, respectively, for object detection. The weighted box fusion removes duplicate detected objects and estimates the optimal object. The proposed network is more stable and efficient in detecting distant objects than existing single-object detection models. Through performance evaluations, the proposed network exhibited an average precision surpassing that of the existing single-object detection models by more than 7%, and the false detection rate was reduced by 59% compared to similar multifocal-based state-of-the-art detection methods.

Healthcare monitoring for humans is important due to several factors including life quality and early detection of health-related problems. Human activity patterns recognition is the most promising ways to monitor human health. Uprisings in the human activity patterns recognition has enabled researchers to recognize multiple health issues through the usage of multiple sensory devices such as motion-based wearable sensors. Irrelevant motion patterns can lead to overlook the important activity recognition in daily living. For this purpose, an early discovery of motion patterns has been proposed for activity recognition in this paper. Main objective is to support the activity detection through motion patterns and deep learning mechanism. The proposed method contains three layered architecture including pre-processing layer, features engineering layer, and classification layer. The anticipated study is investigated over an openly available dataset named Opportunity and results have shown improvement in terms of achieving higher accuracy rate of 88.57%.