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[This corrects the article DOI: 10.1016/j.mex.2024.102820.].
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In computer vision, navigating multi-object tracking in crowded scenes poses a fundamental challenge with broad applications ranging from surveillance systems to autonomous vehicles. Traditional tracking methods encounter difficulties associating noisy object detections and maintaining consistent labels across frames, particularly in scenarios like video surveillance for crowd control and public safety. This paper introduces 'Improved Space-Time Neighbor-Aware Network (STNNet),' an advanced framework for online Multi-Object Tracking (MOT) designed to address these challenges. Expanding upon the foundational STNNet architecture, our enhanced model incorporates deep reinforcement learning techniques to refine decision-making. By framing the online MOT problem as a Markov Decision Process (MDP), Improved STNNet learns a sophisticated policy for data association, adeptly handling complexities such as object birth/death and appearance/disappearance as state transitions within the MDP. Through extensive experimentation on benchmark datasets, including the MOT Challenge, our proposed Improved STNNet demonstrates superior performance, surpassing existing methods in demanding, crowded scenarios. This study showcases the effectiveness of our approach and lays the groundwork for advancing real-time video analysis applications, particularly in dynamic, crowded environments. Additionally, we utilize the dataset provided by STNNET for density map estimation, forming the basis for our research.â¢Develop an advanced framework for online Multi-Object Tracking (MOT) to address crowded scene challenges, particularly improving object association and label consistency across frames.â¢Explore integrating Deep Reinforcement learning techniques into the MOT framework, framing the problem as an MDP to refine decision-making and handle complexities such as object birth or death and appearance or disappearance transitions.
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Maintaining an optimal stress level is vital in our lives, yet many individuals struggle to identify the sources of their stress. As emotional stability and mental awareness become increasingly important, wearable medical technology has gained popularity in recent years. This technology enables real-time monitoring, providing medical professionals with crucial physiological data to enhance patient care. Current stress-detection methods, such as ECG, BVP, and body movement analysis, are limited by their rigidity and susceptibility to noise interference. To overcome these limitations, we introduce STRESS-CARE, a versatile stress detection sensor employing a hybrid approach. This innovative system utilizes a sweat sensor, cutting-edge context identification methods, and machine learning algorithms. STRESS-CARE processes sensor data and models environmental fluctuations using an XG Boost classifier. By combining these advanced techniques, we aim to revolutionize stress detection, offering a more adaptive and robust solution for improved stress management and overall well-being.â¢In the proposed method, we introduce a state-of-the-art stress detection device with Galvanic Skin Response (GSR) sweat sensors, outperforming traditional Electrocardiogram (ECG) methods while remaining non-invasiveâ¢Integrating machine learning, particularly XG-Boost algorithms, enhances detection accuracy and reliability.â¢This study sheds light on noise context comprehension for various wearable devices, offering crucial guidance for optimizing stress detection in multiple contexts and applications.