Human detection and tracking systems using OpenCV

In the context of increasing security threats such as terrorism, advanced video surveillance systems are becoming essential. These systems not only detect human presence, but also analyze behavior, helping to prevent potentially dangerous incidents. In recent years, the development of people detection and tracking systems has progressed significantly, providing real-time solutions. From the point of view of the state of the art, there is no perfect algorithm for foreground segmentation to be adaptable to difficult situations, such as strong shadow, sudden change of light, shaking of trees and so on. Most people detection and tracking systems work well in environments with gradual light change, however, they fail to cope with sudden light change, shaking trees, and moving backgrounds.

In any system, the background subtraction approach is used for foreground detection. After background subtraction, shadow detection is applied. To filter out camera noise and irregular object motion, morphological operations are used following shadow detection. Then the foreground mask image is formed. After that, the blobs are segmented from the foreground mask image. Due to noises, an object may include several blobs. Blob merging is used to form the whole object after blob segmentation. There are two ways of human classification: one way is to use codebooks to recognize whether the blob is human or not; another way is to track the blob, if the blob is tracked successfully, this object is human. The aspect-based tracking approach is used for blob tracking and human tracking. The two types of false object detection are used to reduce the false alarm and adjust the background pattern. The system architecture is described in fig.1.

Algorithms used:

• Background subtraction: This is a technique to separate moving objects from the static background of a scene. It works by comparing each frame of the video to a background pattern and spotting the differences. These differences are considered to be part of the foreground.
• Shadow detection and morphological operations: After identifying the foreground, shadow detection helps distinguish shadows from real objects. Morphological operations such as erosion and dilation are applied to improve image quality by removing noise and strengthening the structure of detected objects.
• Histogram of Oriented Gradients (HOG): HOG extracts features from images by computing directional gradients in various portions of the image. These features are then used to identify specific shapes, such as the outline of a person.
• Support Vector Machines (SVM): SVM is a classification technique that uses a training data set to determine a hyperplane that best separates different classes. In the case of human detection, SVM classifies the data based on the features extracted by HOG as human or non-human.
• Appearance-based methods: These methods focus on the appearance and shape of objects to track them over time. Tracking is based on comparing the appearance of the object in consecutive frames, thus maintaining coherent tracking.

These systems have wide applications in areas such as security, traffic monitoring and human behavior analysis. The ability to detect and track people in real time can help prevent security incidents and improve the management of public spaces.