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Motion detection algorithms are the basis for a wide range of applications in computer vision like visual surveillance, object recognition and tracking and compression of video streams.
The most common approach for motion detection in surveillance systems with static cameras are the so called background subtraction algorithms. In these algorithms, a (moving) foreground object is detected by comparing the current image with the static background of the scene. The acquisition of this background image is the main challenge of background subtraction algorithms, since the background image might not be static but has to adapt to several changes as:
Unless implemented otherwise in hardware, video motion detection is an analysis of consequently captured video frames and comparison in order to detect mismatching areas. As a video processing algorithm, it has its accuracy and possible errors of two types: false alarm and lost event. More accurate detection is typically expected to have smaller rate for both types, however the cost of error may be different, and so are the reasons that may cause the errors.
Both types of errors have negative impact on the overall motion based surveillance, including live observation and monitoring, and effective storage and archive navigation. False alarms lead to recording of useless footage, jamming archive with useless events so that proper events become difficult to find and locate. In an environment with ring buffer recording false alarms directly affect length of video archive forcing footage to be deleted for new [false] recordings which would not be recorded if motion detection accuracy was higher. Lost even type of detection error leads to loss of footage for a scene of interest at all.
False alarms are typically caused by video feed changes that are technically changes (that is, for example, pixel brightness level changes) but human observer does not percept them as such because being able to interpret the scene he is ignoring the changes as unimportant, or even does not register as changes at all. These picture changes might include:
To address mentioned conditions and avoid false alarms, motion detection algorithms typically implement certain techniques and provide user configurable settings which may include:
However the measures addressed to decrease false alarm error rate level may appear to be the cause of lost event error and effective of a motion detector is a trade-off between the two. The most widespread configurable setting (in many cases it is the only available setting) that enables an ability to adjust the detector is sensitivity. A more sensitive detector would register more motion, it would give more false alarms and less lost events. A less sensitive detector would register fewer events and lose more events, but the ones registered would most probably be real events, not false alarms.
A proper design of a motion based surveillance and recording system should start from proper choice of hardware and camera positioning to avoid capturing video with high false alarm factors in the view. It is important to realize that software will be unlikely to accurately compensate a severe mistake in layout and hardware model in first place and motion detection has to be kept in mind from the very start.
It should also be noted that Motion Detection and Video Encoding are very CPU intensive process. Care should be taken that your PC is capable of performing these operations without maxing CPU consumption to maximum level and lowering effective capture frame rates. This can be remedied by either:
In order achieve highest motion detection accuracy it is important to configure sensitivity options on a per camera basis. At the highest sensitivity level the algorithm will trigger at the smallest change in light or movement, whereas at the lowest level it is necessary to walk in front of the camera to trigger it. Environmental conditions also need to be taken into account. For instance if you have a moving background like blowing curtains a high sensitivity setting would not be appropriate as it would trigger each time there was a gust of wind. Remember when it comes to configuring your camera's sensitivity the two factors which must be taken into consideration are (a) light, (b) movement.
Anionu's surveillance settings offer options to record on motion, maximum recording duration, how long to record after the cessation of motion and how long to wait before the system can record again after cessation of motion. The recording settings should be carefully considered against desired usage scenario and it is advised that both post-motion recording and recording without motion are enabled because of the following reasons. Recording motion only frames may result in situation when video frames recorded during shot motion interval are blurred, too dark or too light or captured an unfortunate moment of the scene. Recording a few extra video frames has barely noticeable impact on storage costs however provides additional information on motion alarm event and smooth video at the time of event. Very often this is useful and helpful during investigation of the recorded scene.
* This will not affect the recording frame rate which always captures at the maximum possible frame rate up to 20fps.