多层时空切片联合的快速人体跟踪算法

针对传统人体跟踪方法中目标模型复杂、计算量大等问题，该文提出一种无目标模型的多层时空切片联合的人体跟踪算法。用多层时空切片中的多个动态区域表示人体，区域的选择无需使用任何预定义的目标区域模型。使用时空切片方法在图像序列空间中提取多层水平时空切片图像，在每层时空切片图像中，检测和跟踪潜在的运动区域，并根据区域运动一致性和空间一致性关系，将多个区域关联成不同的人体目标，实现多个人体目标的跟踪，从而将XYT 3维空间中的人体跟踪问题转化为多个XT 2维空间的区域联合跟踪问题。实验表明，该算法降低了跟踪的轨迹误差，满足实时性跟踪要求，同时通过多区域的联合增强了跟踪算法的抗干扰能力，即在人体部分区域丢失的情况下仍能有效跟踪。

Combination of Multiple Spatial-temporal Slices for Fast Human Tracking

Considering the high computational cost and complex object representation problems in human tracking, this paper presents a model-free tracking approach using a combination of multiple spatial-temporal slices. The human is represented with a variable number of components in different spatial-temporal slice images. The component initialization requires no pre-defined object part model. By introducing the spatial-temporal slice method, the original image sequence volume is divided into multiple horizontal spatial-temporal slice images. In each slice image, candidate components are detected and tracked across frames. A combination scheme is proposed to assemble these components into different human objects based on their motion and position consistence. Thus, the traditional human tracking issue in the XYT 3D space is transformed into a combined component tracking issue in the XT 2D space. Experiments show that the proposed method reduces the trajectory errors, is real-time computational efficient and robust to human component missing.