Presently in most of the real world applications like video surveillance systems, human activities are captured and retained as multimodal information for authorized permitted actions. However the degree of accuracy in recognition of such actions greatly depends on many factors, including occlusion, illumination factor, cluttered environment, and so on. In this work we propose the correlation of temporal difference frame (CTDF) algorithm which captures the local maxima’s of every small movement and its neighboring information. Temporal difference obtained between frames, block size defined to obtain the surround information and finally, the comparison of one to all points between identified frames greatly increase the accuracy. The algorithm takes in the raw video input of the standard UT interaction and BIT interaction datasets. Features extracted using the proposed algorithm is passed through variants of SVM which gives state of art results, 95.83% accuracy for UT Interaction and an accuracy of 90.4% for BIT interaction dataset.

当前，在诸如视频监视系统的大多数现实世界应用中，人类活动被捕获并保留为用于授权许可动作的多模式信息。但是，识别此类动作的准确性很大程度上取决于许多因素，包括遮挡，照明因素，混乱的环境等。在这项工作中，我们提出了相关的时差帧（CTDF）算法，该算法捕获每个小运动的局部最大值及其邻近信息。在帧之间获得时间差异，定义块大小以获得环绕信息，最后，将识别帧之间的一个点与所有点进行比较，可以大大提高准确性。该算法接收标准UT交互和BIT交互数据集的原始视频输入。