Multiple Object Tracking(MOT)is essential for applications such as autonomous driving,surveillance,and analytics;However,challenges such as occlusion,low-resolution imaging,and identity switches remain persistent.We p...Multiple Object Tracking(MOT)is essential for applications such as autonomous driving,surveillance,and analytics;However,challenges such as occlusion,low-resolution imaging,and identity switches remain persistent.We propose HAMOT,a hierarchical adaptive multi-object tracker that solves these challenges with a novel,unified framework.Unlike previous methods that rely on isolated components,HAMOT incorporates a Swin Transformer-based Adaptive Enhancement(STAE)module—comprising Scene-Adaptive Transformer Enhancement and Confidence-Adaptive Feature Refinement—to improve detection under low-visibility conditions.The hierarchical DynamicGraphNeuralNetworkwith TemporalAttention(DGNN-TA)models both short-and long-termassociations,and the Adaptive Unscented Kalman Filter with Gated Recurrent Unit(AUKF-GRU)ensures accurate motion prediction.The novel Graph-Based Density-Aware Clustering(GDAC)improves occlusion recovery by adapting to scene density,preserving identity integrity.This integrated approach enables adaptive responses to complex visual scenarios,Achieving exceptional performance across all evaluation metrics,including aHigher Order TrackingAccuracy(HOTA)of 67.05%,a Multiple Object Tracking Accuracy(MOTA)of 82.4%,an ID F1 Score(IDF1)of 83.1%,and a total of 1052 Identity Switches(IDSW)on theMOT17;66.61%HOTA,78.3%MOTA,82.1%IDF1,and a total of 748 IDSWonMOT20;and 66.4%HOTA,92.32%MOTA,and 68.96%IDF1 on DanceTrack.With fixed thresholds,the full HAMOT model(all six components)achieves real-time functionality at 24 FPS on MOT17 using RTX3090,ensuring robustness and scalability for real-world MOT applications.展开更多
Fuel station drive-offs,wherein the drivers simply drive off without paying,are a major issue in the UK(United Kingdom)due to rising fuel costs and financial hardships.The phenomenon has increased greatly over the las...Fuel station drive-offs,wherein the drivers simply drive off without paying,are a major issue in the UK(United Kingdom)due to rising fuel costs and financial hardships.The phenomenon has increased greatly over the last few years,with reports indicating a substantial increase in such events in the major cities.Traditional prevention measures such as Avutec and Driveoffalert rely primarily on expensive infrastructure and blacklisted databases.Such systems typically involve costly camera installation andmaintenance and are consequently out of the budget of small fuel stations.These conventional approaches also fall short regarding real-time recognition,particularly regarding first-time impostors using fictitious plates,which represent an increasingly significant proportion of such forgery.This research presents an AI(Artificial Intelligence)-driven detection system using the MOT(Ministry of Transport)History API(Application Programming Interface)to scan in real-time at gas stations to recognize and prevent such fraud.The system integrates various state-of-the-art technologies to offer a foolproof system.Using the latestYOLO(YouOnly Look Once)model to recognize number plates and EasyOCR(Optical Character Recognition)to recognize characters,the system correctly reads license plates in various environmental conditions like lighting,viewpoint,and weather conditions.This approach minimizes the utilization of expensive camera systems and employs cheaper ANPR(AutomaticNumber Plate Recognition)gear,availing existing installed surveillance cameras on filling stations.The system operates with a basic web-based application to notify operators of stolen vehicles in real-time,enabling them to react immediately.Real-world testing achieves 84%success with CCTV(Closed-Circuit Television)images,depicting its real-world applicability.The results indicate that the AI-driven solution offers a monumental leap compared to current practices,giving fuel stations a cost-effective and efficient means of reducing financial loss from drive-off incidents.展开更多
The purpose of this study was to observe record and compare the children＇s of early childhood performance of motor skills of different nationalities from Greece, Albania and Sweden. Additionally to investigate differ...The purpose of this study was to observe record and compare the children＇s of early childhood performance of motor skills of different nationalities from Greece, Albania and Sweden. Additionally to investigate differences in motor performance between boys and girls and between age groups. The survey was conducted in the school years 2013-2014 and 2014-2015, and took place in the flame of the student exchange program ERASMUS internships of Preschool Education, University of loannina. The sample consisted of 369 infants （187 boys, 182 girls） aged 66 ± 7 months. The sample was selected according to the access that the team had in nursery schools of Ioannina （N1 = 133）, in Dervitsani （N2 = 131） Albania and Gothenburg （N2 = 105） of Sweden. The array of 18 different motor activities for children aged 4-6 years old was used to investigate the toddlers＇ degree of movement performance. They were used the manufacturer＇s instructions for the degree of movement performance and classification of the sample in different categories, while a descriptive statistics and analysis of variance （Three Way ANOVA） took place for sex factors, age and region of origin. The results showed that although there were differences in rates distributions and averages, there were no significant differences either between children from the three countries or between boys and girls, however, there were in the age groups with older children who achieved better rates.展开更多
基金supported in part by Multimedia University under the Research Fellow Grant MMUI/250008in part by Telekom Research&Development Sdn Bhd under Grants RDTC/241149 and RDTC/231095+1 种基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R140)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Multiple Object Tracking(MOT)is essential for applications such as autonomous driving,surveillance,and analytics;However,challenges such as occlusion,low-resolution imaging,and identity switches remain persistent.We propose HAMOT,a hierarchical adaptive multi-object tracker that solves these challenges with a novel,unified framework.Unlike previous methods that rely on isolated components,HAMOT incorporates a Swin Transformer-based Adaptive Enhancement(STAE)module—comprising Scene-Adaptive Transformer Enhancement and Confidence-Adaptive Feature Refinement—to improve detection under low-visibility conditions.The hierarchical DynamicGraphNeuralNetworkwith TemporalAttention(DGNN-TA)models both short-and long-termassociations,and the Adaptive Unscented Kalman Filter with Gated Recurrent Unit(AUKF-GRU)ensures accurate motion prediction.The novel Graph-Based Density-Aware Clustering(GDAC)improves occlusion recovery by adapting to scene density,preserving identity integrity.This integrated approach enables adaptive responses to complex visual scenarios,Achieving exceptional performance across all evaluation metrics,including aHigher Order TrackingAccuracy(HOTA)of 67.05%,a Multiple Object Tracking Accuracy(MOTA)of 82.4%,an ID F1 Score(IDF1)of 83.1%,and a total of 1052 Identity Switches(IDSW)on theMOT17;66.61%HOTA,78.3%MOTA,82.1%IDF1,and a total of 748 IDSWonMOT20;and 66.4%HOTA,92.32%MOTA,and 68.96%IDF1 on DanceTrack.With fixed thresholds,the full HAMOT model(all six components)achieves real-time functionality at 24 FPS on MOT17 using RTX3090,ensuring robustness and scalability for real-world MOT applications.
文摘Fuel station drive-offs,wherein the drivers simply drive off without paying,are a major issue in the UK(United Kingdom)due to rising fuel costs and financial hardships.The phenomenon has increased greatly over the last few years,with reports indicating a substantial increase in such events in the major cities.Traditional prevention measures such as Avutec and Driveoffalert rely primarily on expensive infrastructure and blacklisted databases.Such systems typically involve costly camera installation andmaintenance and are consequently out of the budget of small fuel stations.These conventional approaches also fall short regarding real-time recognition,particularly regarding first-time impostors using fictitious plates,which represent an increasingly significant proportion of such forgery.This research presents an AI(Artificial Intelligence)-driven detection system using the MOT(Ministry of Transport)History API(Application Programming Interface)to scan in real-time at gas stations to recognize and prevent such fraud.The system integrates various state-of-the-art technologies to offer a foolproof system.Using the latestYOLO(YouOnly Look Once)model to recognize number plates and EasyOCR(Optical Character Recognition)to recognize characters,the system correctly reads license plates in various environmental conditions like lighting,viewpoint,and weather conditions.This approach minimizes the utilization of expensive camera systems and employs cheaper ANPR(AutomaticNumber Plate Recognition)gear,availing existing installed surveillance cameras on filling stations.The system operates with a basic web-based application to notify operators of stolen vehicles in real-time,enabling them to react immediately.Real-world testing achieves 84%success with CCTV(Closed-Circuit Television)images,depicting its real-world applicability.The results indicate that the AI-driven solution offers a monumental leap compared to current practices,giving fuel stations a cost-effective and efficient means of reducing financial loss from drive-off incidents.
文摘The purpose of this study was to observe record and compare the children＇s of early childhood performance of motor skills of different nationalities from Greece, Albania and Sweden. Additionally to investigate differences in motor performance between boys and girls and between age groups. The survey was conducted in the school years 2013-2014 and 2014-2015, and took place in the flame of the student exchange program ERASMUS internships of Preschool Education, University of loannina. The sample consisted of 369 infants （187 boys, 182 girls） aged 66 ± 7 months. The sample was selected according to the access that the team had in nursery schools of Ioannina （N1 = 133）, in Dervitsani （N2 = 131） Albania and Gothenburg （N2 = 105） of Sweden. The array of 18 different motor activities for children aged 4-6 years old was used to investigate the toddlers＇ degree of movement performance. They were used the manufacturer＇s instructions for the degree of movement performance and classification of the sample in different categories, while a descriptive statistics and analysis of variance （Three Way ANOVA） took place for sex factors, age and region of origin. The results showed that although there were differences in rates distributions and averages, there were no significant differences either between children from the three countries or between boys and girls, however, there were in the age groups with older children who achieved better rates.
