Human detection in surveillance system

In the last decade, the task of human detection rises to be an integral part in various real applications especially in areas that require surveillance [7, 8], due to the large amount of visual data that the outcome of these applications produce which need to be processed and managed.

In video surveillance systems used to identify and detect human objects there must be someone or something monitoring the video sequence. This is usually done by a human operator, this operator has to monitor the stream of records captured from the surveillance cameras and displayed on many screens, in order to detect the abnormal behaviour. Because human operators are very good and efficient at recognising positions, it will do so as long as the operators are able to focus and watch all the screens in a short time [6]. Clearly there is a limit to how much one person can effectively follow and watch all at the same time, and with the installation of more cameras, more human resources are needed. For example, human abnormal behaviour detection in surveillance systems is widely used in many real time applications, and it has become a crucial need for security purposes, because detecting

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abnormal actions robustly, increases the opportunity to avoid accidents and may be acquired by triggering such alarms or signals to the surveillance system operators.

Identifying abnormal behaviour can be different in many applications, that's because every application environment has its specifications of abnormal behaviour. These abnormal behaviours or actions can be such as people running in a specific place at the same time, someone holds illegal items in their hand, or someone jumping in a secure section, and so on. Figure 6.3 below show some example of abnormal human behaviours.

Figure 6.1 Some example of human abnormal behaviours.

The first step in detecting human abnormality behaviours using surveillance systems, is to detect the human object in an image or video frame, in order to classify the behaviour as normal or abnormal, so that the needs of such an approach with high accuracy for classifying the located object as a human is very important for further process abnormality detection or tracking.

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An example of human abnormality detection is to detect the abnormal behaviours of humans (students) in academic scenarios, this case study can be implemented in real time applications by following a sequence of steps in order to detect the human and then classify the human behaviour as normal behaviour or abnormal.

The below figure shows the general follow diagram for abnormality detection.

In this case study, the aim is to detect the abnormal behaviour of the students and to classify the identity of the student who did the abnormal behaviour, to do this, it requires a pre-phase to collect the student pictures and their details from the students records, and then from the students pictures, the unique features are extracted for each student and it is stored with the corresponding student details in a database to be used in identifying the student.

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From the general flow diagram, the first step is to obtain the video stream, and this can be captured by several kinds of surveillance system cameras, then this video stream is divided into 32 frames as the normal number of frames.

The second step is to locate the region of the abnormality, and detect the objects that are causing this abnormal behaviour so that a temporal differencing approach can been used to detect the regions of the abnormality and detect the object who causes it.

After that a binary statistical erosion function was applied to remove any noise that can affect the detection.

By using this the objects can be detected however, to classify the detected object as human or non-human, the similarity pattern matching was used by applying the Omega equation which presents a spatial pattern called S pattern. Using this S pattern, the similarity pattern matching process runs in order to classify the human and ignore other objects. The following figure show the flow diagram used to localise and detect objects.

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The main steps of the shape model is summarised based on the OMEGA equation as the following:

The results of the above steps is to present the S pattern which can be used to classify the detected object as human or non-human based on the similarity of shape matching. Figure 6.4 shows the presented S pattern.

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After classifying the detected object as human, the next step is to analyse the activity of the human in terms of what’s normal activity or abnormal activity, in order to this to do this the activity features are extracted by using the support vector machine approach to classify the abnormal activity of a human, the general steps for analysing the human activity shown in Figure 6.5.

Figure 6.5 General steps for analysis the human activity

The main idea of the support vector machine is to divide the data set into different groups based on finding the HYPERPLANE and then the furthest group with the closest points to the class. Figure 6.6 shows thedistance of group using the HYPERPLANE based on support vector machine.

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Figure 6.6 The choosing distance of group using the HYPERPLANE based on support vector machine.

The next step after classifying the human activity, in cases were the human activity is classified as abnormal, a trigger alarm will be sent directly to the security team for security purposes, and a picture of the person who is causing the abnormal activity will be obtained and send forward to the information retrieval process.

In the information retrieval process, the obtained image of the person’s features will be extracted to find the matching features between this person features and the dataset of all the students features in order to identify the person.

Figure 6.7 shows the flow of steps for the retravel of information in order to extract the details and identity the person who is causing the abnormal activity.

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Figure 6.7 The flow steps for information retravel in order to extract the details and identity of the person who causes the abnormal activity.