# Real-Time, Content-Based Communication Load Reduction in the Internet of Multimedia Things
## 2 Related Research and Background
### Video Processing
#### 2.2.2 Motion Detection  
A very important step in video processing is motion detection, which is also used for video surveillance and alarm systems. Motion detection can serve as a basis for video analysis including object detection, human and object tracking, counts of moving and stationary objects in a scene and separation of foreground from background. Frame differencing, background subtraction, and optical flow methods have been used. In fact, sometimes integration of these methods has helped in detectingmoving objects. A comparative study of general and commonly used motion detection methods can be found in Reference [42]. Background subtraction is the simplest method among them [43]. A motion detection mechanism was described in Reference [44], which detects motion using multiple camera sensors and checks for abnormal motion detected by the cameras. Motion detected by all cameras is checked for inter-connectivity, that is, if a detector is isolated spatially, but has connectivity to other detectors temporally, it is not eliminated. If a detector is isolated both spatially and temporally, it is removed. Results of classification of detectors based on detected motion are also illustrated. Videos in the compressed domain can be used for additional operations on videos such as feature extraction, object detection, segmentation, and watermarking. If motion detection is carried out on a video in the compressed domain, that motion detection can be made more efficient by utilizing the data acquired after compression, consisting of features such as macroblocks, motion vectors and DCT coefficients [45]. A dynamic template matching algorithm that is adaptive to changes in the video frames is presented in Reference [46], updating a reference image every time a change is detected. The form of temporal differencing used in Reference [47] solves the problem that the motion represented using consecutive temporal difference shows images with unnatural effects that is, mild movements of leaves, trees and effects of air are also detected as motion. Morphological operations and area computation of connected fields are used to eliminate unwanted areas of motion. Consecutive frame differencing, grayscale transformation of the resulting difference frame and its binary representation yields approximately the same results as in Reference [47] but with the limitation of detecting air and illumination effects as motion [48]. The research [48] indicates that with a combination of an image subtraction method and edge detection mechanism, better motion detection is achieved. The methods described include image subtraction, image subtraction followed by edge detection and edge detection followed by image subtraction, among which image subtraction after edge detection is best [49]. Another motion detection setup for four cameras using frame differencing, pixelate filter, blob count and morphological filtering is given in Reference [50]. A dynamic motion detection technique is also given in Reference [51] to find active and inactive regions and another technique with weighted difference image and its binary conversion can be found in Reference [52], which finds motion vectors to help locate important regions of motion. Background subtraction can be used for motion detection by separating background and foreground and then, by masking the binary color space, objects can be further classified as background and foreground [53]. The authors of Reference [54] propose an hierarchical data structure and background subtraction method of motion detection. The hierarchical data structure method was updated, as it now needs comparison of only one pixel per sub-image, thus, reducing processing overhead. Additionally, the background-updating mechanism is changed by introducing an interval for updating the background image, which is based on how small or large change is detected between background and current image. Other variations in motion detection methods include use of DCT [55] and entropy based detection of motion [56].