Left object and lost object real-time detection method based on embedded system

Abstract

The invention discloses a left object and lost object real-time detection method based on an embedded system. The left object and lost object real-time detection method based on the embedded system comprises the following steps of obtaining video data, extracting a suspicious static target object area on the basis of backgrounds of images, to be detected, of long-and-short period Gaussian mixture model learning video data, and obtaining a target foreground binary image; carrying out accumulation timekeeping on the retention time of the suspicious static target object area according to the target foreground binary image, carrying out triggering to give an alarm if the retention time exceeds a preset threshold in a preset alarm time, and marking size dimension information and specific position information of a suspicious static target object on an original image; determining whether the suspicious static target object area contains a detected target or not through analysis of the shaking conditions of the edge of the suspicious static target object area, and finally obtaining a rectangular target area; distinguishing a left object and a lost object in the rectangular target area containing the detected target, and giving an alarm. The left object and lost object real-time detection method based on the embedded system is low in operation overhead, can be implemented in an embedded SOC leading end, and the requirement for real-time detection can be met.

Description

technical field [0001] The invention relates to the application of image processing and video intelligent analysis in the security field and intelligent transportation, in particular to a real-time detection method for leftovers and lost objects based on an embedded system. Background technique [0002] With the development of social economy, people's demand for safety precautions is also increasing. For example, airports, banks, subways, waiting halls and other important places with a large number of people are easy to be used by terrorists, and usually have great security risks, so real-time monitoring is required. The traditional monitoring system can only be used for post-event evidence presentation, but cannot detect the occurrence of abnormal events in real time. The real-time detection of leftovers and lost objects is used to assist monitoring personnel to monitor whether suspicious objects are left behind in the scene, and whether important objects in the scene have...

AI Technical Summary

Problems solved by technology

In the current detection method, there are mainly two problems: one is that the algorithm is mainly oriented to the server side, and the occupied memory and CPU operation are relatively large, so it cannot be transplanted to the embedded platform for real-time detection. The requirements are higher; the second is that the performance of the current detection methods is still insufficient. Most of them are only suitable for the detection of indoor unchanging scenes, and cannot realize all-weather detection. They are less stable for outdoor complex scene changes, especially lighting changes and temporary pedestrian vehicles. It is easy to cause false detection and false alarm, these problems need to be further solved

Images