  In this paper we investigate a robust method to identify anomalies in complex
scenes. This task is performed by evaluating the collective behavior by
extracting the local binary patterns (LBP) and Laplacian of Gaussian (LoG)
features. We fuse both features together which are exploited to train an MLP
neural network during the training stage, and the anomaly is identified on the
test samples. Considering the challenge of tracking individuals in dense
crowded scenes due to multiple occlusions and clutter, in this paper we extract
LBP and LoG features and use them as an approximate representation of the
anomalous situation. These features well match the appearance of anomaly and
their consistency, and accuracy is higher both in regular and irregular areas
compared to other descriptors. In this paper, these features are exploited as
input prior to train the neural network. The MLP neural network is subsequently
explored to consider these features that can detect the anomalous situation.
The experimental tests are conducted on a set of benchmark video sequences
commonly used for anomaly situation detection.
