Human and animal interacting events in video image frames will leads to anomalies and it cannot be predicted. These anomalous events was happened due to the interactions of human, animals and birds with each other. Some of the human and animal anamolous interactions will leads to anomalous actions in camera surveillance sites and are to be considered as a serious issue. All anomalies will leads the system or the authorities who dedicated for monitoring to the suspicious events. To detect the anomalies, the animals and human objects must be identified in each image frames first. After object identification events detection phase has to be done. Before going to the event detection phase, noise elimination, shadow detection, object classification can be carried out according to the needs. This paper reveals general detection methodology for animal detection and human crowd. The proposed work mainly concentrated on the detection of animals in human territory based on its palm print images and video images. Human crowd can be treated as smoke- screen for all types of anomalies and so this paper mainly concentrated to detect the crowd well in advance and thereby preventing the crowd before it is happening

Crowd is a huge number of people meet jointly in a disorganized or unmanageable way. Crowd in any atmosphere may leads to suspicious events. Nobody can predict the crowd and some anomalies might happen in the presence of crowd. So prevention of crowd well in advance is the only remedy to tackle the situation. Advance crowd detection is an important subfield in video surveillance. Prior detection or prevention of crowd has so much of importance while we are considering the present scenarios all over the world. So now-days, an automatic crowd prevention technique is needed for all the countries to protect their land, provide safety for their citizens and law enforcement. Crowd prevention system using manual operators are weak due to many physiological and non-physiological factors but it will provide better performance than automatic system in case of decision making. Many models have been developed so far to detect the crowd automatically. Our system aims to predict the crowd well in advance in three levels and so the automatic system or the operator will get enough time to respond or take a decision. To detect the formation of crowd well in advance, all the human objects in a frame was identified by Gaussian mixture model and object classification, shadow was eliminated and crowd was predicted using the object rectangle model and center vertical line model. The pixel distance between the each rectangles and center line is used to predict the formation of crowd. This paper also gives some suggestions to crowd modelling.