Abstract
Vision-based activity recognition is essential for security, monitoring and surveillance applications. Further, real-time analysis having low-quality video and contain less information about surrounding due to poor illumination, and occlusions. Therefore, it needs a more robust and integrated model for low quality and night security operations. In this context, we proposed a hybrid model for illumination invariant human activity recognition based on sub-image histogram equalization enhancement and k-key pose human silhouettes. This feature vector gives good average recognition accuracy on three low exposure video sequences subset of original actions video datasets. Finally, the performance of the proposed approach is tested over three manually downgraded low qualities Weizmann action, KTH, and Ballet Movement dataset. This model outperformed on low exposure videos over existing technique and achieved comparable classification accuracy to similar state-of-the-art methods.
Abstract (translated)
基于视觉的活动识别对于安全、监控和监视应用至关重要。此外，实时分析具有低质量的视频，由于光照差和闭塞，包含的周围信息较少。因此，它需要一个更强大和集成的低质量和夜间安全操作模型。在此背景下，我们提出了一种基于子图像直方图均衡增强和K键人体轮廓的光照不变人体活动识别混合模型。该特征向量对原始动作视频数据集的三个低曝光视频序列子集具有较好的平均识别精度。最后，在三个手动降级的低质量Weizmann动作、KTH和芭蕾舞动作数据集上测试了该方法的性能。与现有技术相比，该模型在低曝光视频方面表现出色，并取得了与同类最先进方法相当的分类精度。
URL
https://arxiv.org/abs/1903.04090
