Security and safety remain paramount concerns for both governments and individuals worldwide.In today’s context,the frequency of crimes and terrorist attacks is alarmingly increasing,becoming increasingly intolerable...Security and safety remain paramount concerns for both governments and individuals worldwide.In today’s context,the frequency of crimes and terrorist attacks is alarmingly increasing,becoming increasingly intolerable to society.Consequently,there is a pressing need for swift identification of potential threats to preemptively alert law enforcement and security forces,thereby preventing potential attacks or violent incidents.Recent advancements in big data analytics and deep learning have significantly enhanced the capabilities of computer vision in object detection,particularly in identifying firearms.This paper introduces a novel automatic firearm detection surveillance system,utilizing a one-stage detection approach named MARIE(Mechanism for Realtime Identification of Firearms).MARIE incorporates the Single Shot Multibox Detector(SSD)model,which has been specifically optimized to balance the speed-accuracy trade-off critical in firearm detection applications.The SSD model was further refined by integrating MobileNetV2 and InceptionV2 architectures for superior feature extraction capabilities.The experimental results demonstrate that this modified SSD configuration provides highly satisfactory performance,surpassing existing methods trained on the same dataset in terms of the critical speedaccuracy trade-off.Through these innovations,MARIE sets a new standard in surveillance technology,offering a robust solution to enhance public safety effectively.展开更多
多目标跟踪技术对猪只精细化养殖具有重要意义。针对饲养环境差异、猪只的快速移动以及群猪之间的频繁遮挡带来的多目标跟踪挑战,该研究提出了一种基于Byte的生猪多目标跟踪算法UKFTrack。首先,构建了一个采用定向边界框(oriented bound...多目标跟踪技术对猪只精细化养殖具有重要意义。针对饲养环境差异、猪只的快速移动以及群猪之间的频繁遮挡带来的多目标跟踪挑战,该研究提出了一种基于Byte的生猪多目标跟踪算法UKFTrack。首先,构建了一个采用定向边界框(oriented bounding box,OBB)标注的多样化数据集,涵盖了猪只多种运动模式以及不同饲养场景和猪群密度;其次,引入了无迹卡尔曼滤波以更好地适配OBB标注,并对传统的状态向量进行扩展,新增了角度和角速度参数,设计了残差函数处理角度变量以避免直接相减所造成的误差。最后,提出了一种多阶段匹配策略,通过多次轨迹关联和补充匹配机制,确保在遮挡严重或剧烈运动的情况下,仍能保持对目标的持续跟踪。试验结果表明,在白天重度密集、白天极度密集、夜间重度密集和夜间极度密集4种复杂场景下,UKFTrack的高阶跟踪精度(higher order tracking accuracy,HOTA)分别为96.10%、83.10%、76.50%和84.00%,IDF1得分(identification F1 score)分别为95.70%、78.20%、70.10%和77.60%。相较于StrongSORT,UKFTrack的HOTA分别提高了1.2、13.3、5.9和6.3个百分点,IDF1分别提高了0.1、10.9、5.4和7.4个百分点。因此,该研究提出的跟踪算法能实现复杂环境下群体生猪的准确跟踪,且展现出较强的鲁棒性,能为实际应用中猪只行为与健康监测提供可靠的技术支持。展开更多
文摘Security and safety remain paramount concerns for both governments and individuals worldwide.In today’s context,the frequency of crimes and terrorist attacks is alarmingly increasing,becoming increasingly intolerable to society.Consequently,there is a pressing need for swift identification of potential threats to preemptively alert law enforcement and security forces,thereby preventing potential attacks or violent incidents.Recent advancements in big data analytics and deep learning have significantly enhanced the capabilities of computer vision in object detection,particularly in identifying firearms.This paper introduces a novel automatic firearm detection surveillance system,utilizing a one-stage detection approach named MARIE(Mechanism for Realtime Identification of Firearms).MARIE incorporates the Single Shot Multibox Detector(SSD)model,which has been specifically optimized to balance the speed-accuracy trade-off critical in firearm detection applications.The SSD model was further refined by integrating MobileNetV2 and InceptionV2 architectures for superior feature extraction capabilities.The experimental results demonstrate that this modified SSD configuration provides highly satisfactory performance,surpassing existing methods trained on the same dataset in terms of the critical speedaccuracy trade-off.Through these innovations,MARIE sets a new standard in surveillance technology,offering a robust solution to enhance public safety effectively.
文摘多目标跟踪技术对猪只精细化养殖具有重要意义。针对饲养环境差异、猪只的快速移动以及群猪之间的频繁遮挡带来的多目标跟踪挑战,该研究提出了一种基于Byte的生猪多目标跟踪算法UKFTrack。首先,构建了一个采用定向边界框(oriented bounding box,OBB)标注的多样化数据集,涵盖了猪只多种运动模式以及不同饲养场景和猪群密度;其次,引入了无迹卡尔曼滤波以更好地适配OBB标注,并对传统的状态向量进行扩展,新增了角度和角速度参数,设计了残差函数处理角度变量以避免直接相减所造成的误差。最后,提出了一种多阶段匹配策略,通过多次轨迹关联和补充匹配机制,确保在遮挡严重或剧烈运动的情况下,仍能保持对目标的持续跟踪。试验结果表明,在白天重度密集、白天极度密集、夜间重度密集和夜间极度密集4种复杂场景下,UKFTrack的高阶跟踪精度(higher order tracking accuracy,HOTA)分别为96.10%、83.10%、76.50%和84.00%,IDF1得分(identification F1 score)分别为95.70%、78.20%、70.10%和77.60%。相较于StrongSORT,UKFTrack的HOTA分别提高了1.2、13.3、5.9和6.3个百分点,IDF1分别提高了0.1、10.9、5.4和7.4个百分点。因此,该研究提出的跟踪算法能实现复杂环境下群体生猪的准确跟踪,且展现出较强的鲁棒性,能为实际应用中猪只行为与健康监测提供可靠的技术支持。
