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Basics of Video Compression and Motion Analysis
Published in Maheshkumar H. Kolekar, Intelligent Video Surveillance Systems, 2018
Motion detection is the process of detecting a change in the position of an object relative to its background or a change in the background relative to an object. Optical flow is the distribution of apparent velocities of movement of brightness patterns in an image. It can arise from relative motion of objects and the camera. Optical flow gives a description of motion and thus is used in motion detection. Optical flow is very useful to study motion while the object is stationary and the camera is moving, while the object is moving and the camera is stationary, or when both are moving. Optical flow is based on two assumptions: 1) The brightness of the object point is constant over time. 2) Points that are nearby in the frame move in a similar manner. Since video is simply a sequence of frames, the optical flow methods calculate the motion of an object between two frames that are taken at times t1 $ t_{1} $ and t2 $ t_{2} $ , as shown in Figure 2.10.
Motion Analysis
Published in Yu-Jin Zhang, A Selection of Image Analysis Techniques, 2023
Motion detection refers to detecting whether there is motion (including global and local motions) information in the scene. In this case, only a single fixed camera is generally sufficient. A typical example is security surveillance, where any factors that cause changes in the image must be taken into consideration. Of course, because the changes caused by the light are often slower and the changes caused by the movement of the object are often faster, it can be further distinguished.Motion object location and tracking
Motion Detector Using PIR Sensor
Published in Anudeep Juluru, Shriram K. Vasudevan, T. S. Murugesh, fied!, 2023
Anudeep Juluru, Shriram K. Vasudevan, T. S. Murugesh
In this project, you’ll use a PIR sensor to detect movement inside a room or in a constrained space and log the time in an Adafruit feed. Motion detection can be employed in a lot of places like automatic control of electronic appliances (fan, light) inside a room depending on the human presence, remote monitoring of a room or a constrained space without using a camera and much more.
Experimental evaluation of occupancy lighting control based on low-power image-based motion sensor
Published in SICE Journal of Control, Measurement, and System Integration, 2021
Takuya Futagami, Noboru Hayasaka
Image-based sensors, which apply an image processing technique to visible images [8–10], are expected to increase the energy-saving effect by decreasing the time delay while avoiding a false-off. Two widely applied image processing techniques for the occupancy measurement are motion detection, which tracks the change in appearance in an image [11,12], and image-recognition, which utilizes a classifier based on machine learning [13,14]. Frame difference, background subtraction, and optical flow are well-known motion detection methods, whereas classifiers such as a support vector machine (SVM) or deep neural network (DNN) are primarily used to construct image recognition.
Dynamic method to optimize memory space requirement in real-time video surveillance using convolution neural network
Published in The Imaging Science Journal, 2023
Tamal Biswas, Diptendu Bhattacharya, Gouranga Mandal
During surveillance, motion detection is a crucial technique for locating moving objects in the target video. Frame differencing techniques [52, 53], background removal methods [54, 55] and optical methods [56, 57] are a few important approaches for object recognition. There are advantages and disadvantages to each of these approaches. In the proposed method, a modified frame differencing technique is used to identify moving objects. First, we need to convert all colour frames to grey frames for faster processing and to identify the intensity difference between the two frames. Colour images are converted [58, 59] to grey images for further operations as follows: where I(x,y) stands for the pixel’s intensity value in the newly created grey frame. The coordinates of the pixel are shown here as x and y. The red, green and blue values of a coloured pixel are represented by the letters R, G and B. It calculates the intensity value for each pixel in the kth and (k − 5)th frames. As a result, the frame difference value of a certain pixel (x, y) is calculated as follows: where d(I)i denotes the intensity change between the kth and (k − 5)th frames at the (xi, yi) pixel. When the intensity difference becomes greater than a threshold value, it is considered to be suspicious. For the proposed method, a dynamic threshold value is taken into account. The dynamic threshold (th) is calculated as follows: where denotes the average intensity of every pixel in a frame. Figure 2 shows how the two frames differ from one another. The red area in Figure 2(c) denotes the difference between Figure 2(a) and Figure 2(b). The summation of all the suspicious differences in a frame is calculated as follows: where th denotes the threshold value and n denotes the total number of pixels in a frame. From the (k − 5)th to the kth frame, the total cumulative difference (S)cd is now calculated as follows: Hence, the cumulative difference can be used to track the movement of any suspicious entity (object) in a frame.