Detecting the moving objects of special trajectories by fusing laser range scanners and video cameras

在这两年的项目中，取得了以下成果.基于激光扫描数据的运动目标检测、跟踪和分类算法。1)在交叉口等多种运动目标共存的环境中，提出了一种利用激光扫描数据对运动目标进行联合检测、跟踪和分类的算法。2)在仅有行人的环境中，为了精确检测行人的行走模式，估计行人的行走轨迹，定义了详细的行走模型，研究了密集拥挤环境下基于粒子滤波的行人跟踪算法.融合激光与视觉的算法。主要包括以下几个方面：1）图像属性提取; 2）同一人的匹配; 3）基于激光和视频的跟踪。此外，基于激光的跟踪可以 关于我们 不能获得除XY坐标序列以外的其它信息。提出了激光与视觉融合的测量系统、图像属性提取算法、轨迹与图像属性匹配寻找同一运动目标的算法以及激光与视觉融合的跟踪方法.行为分析-寻找特殊轨迹。主要包括以下几个子课题：1）步行参数提取与步态识别，2）个人行为分析，3）群体行为分析，定义了详细的步行模型，提取了步行参数，如步幅、速度和加速度，并提出了基于上述步行参数的个人步态特征识别算法。另一方面，研究了基于步态和轨迹参数的个人步行行为。提出了一种将个体行为分为正常行为和异常行为的算法。此外，还提出了从大量轨迹中寻找主流的算法，并将其应用于先进空调控制中，并进行了仿真验证.为了获得真实的数据，进行了大量的野外试验。本项目中的所有算法都是在真实数据上开发和检验的，旨在真正的应用。少

In this two years project, the following results are achieved.1. Algorithms for moving objects' detection, tracking and classification using laser scan data.The topic is studied in two levels. 1) In the environment such as an intersection, where different kinds of moving objects exist, an algorithm is developed for joint detection, tracking and classification of moving objects using laser scan data. 2) In the environment where there are pedestrians only, in order to precisely detected walking pattern, and estimate walking trajectories, a detailed walking model is defined, and algorithms of tracking pedestrians in dense crowded situations using particle filter are studied.2. Algorithms for fusing laser with vision. It includes the following sub-topics : 1) extracting image attribute; 2) matching for the same person, 3) laser and video based tracking.In the case of dense crowded situation, there are limitations in precise tracking based only on laser data. Also laser-based tracking can n … More ot obtain the other information except the sequence of XY coordinates. So that a measurement system by fusing laser with vision, an algorithm of extracting image attributes, an algorithm of matching on both trajectory and image attributes to look for the same moving objects, and a tracking by fusing both laser and vision are developed.3. Behavior analysis - looking for special trajectories. It includes the following sub-topics : 1) walking parameters extraction and gait recognition, 2) personal behavior analysis, 3) group behavior analysis.A detailed walking model is defined, the walking parameters such as stride, speed and acceleration of both feet are extracted, an algorithm is developed to recognize personal gait characteristics based on the above walking parameters. On the other hand, personal walking behavior is studied based on both gait and trajectory parameters. An algorithm is developed to divide the personal behaviors into normal and abnormal ones. In addition, algorithm is developed to find the main streams from the mass of trajectories, and an application of the results for advanced air-conditioning control is proposed and tested on simulation.4. A number of field experiments are conducted to acquire real data. All the algorithms in this project are developed and examined on the true data and aiming for true applications. Less