  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.
