A real-time non-tracking monitoring video remnant detection method

A technology of monitoring video and detection methods, which is applied in the field of image processing, and can solve problems such as not taking into account changes in objects and not being able to recognize them

Active Publication Date: 2019-04-16
ANHUI UNIVERSITY
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  • Abstract
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  • Application Information

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Problems solved by technology

This method only uses deep learning to determine the final leftover luggage. It does not take into account that changes in light

Method used

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  • A real-time non-tracking monitoring video remnant detection method
  • A real-time non-tracking monitoring video remnant detection method
  • A real-time non-tracking monitoring video remnant detection method

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Experimental program
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Embodiment 1

[0092] image 3 The detection results of our own test video are given, and the scene is a corridor. image 3 It is an experiment done when there is light, and compared with literature [6] and literature [16]. There are two remnants in the video, which are bags and cartons; image 3 It can be seen from the results that the method in this paper can better exclude the area generated by the light than the other two methods when there is light, and can accurately detect the remnants.

Embodiment 2

[0094] We also apply the method in this paper to the i-LIDS [15] dataset and the ABODA [6] dataset for carryover detection. The scenes in the i-LIDS dataset are divided into three levels: simple, medium, and complex with the change of light and traffic. These three scenarios are tested separately in this paper. And compared with literature [6] and literature [16]. Figure 4 The difficulty of target detection from top to bottom: simple (AB-Easy), medium (AB-Medium), complex (AB-Hard). In the video AB-Easy, the objects left behind in the scene are relatively large, which is relatively easy to detect; in the video AB-Medium, the objects are small and relatively far away, and the detection difficulty is moderate; in the video AB-Hard, the scene is more complicated, objects are farther away, and people More, the detection is more difficult. Figure 4 (a), (4)b, and (4)c are the frames when the remnants actually occurred in the three videos, respectively, and the blue font in the...

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Abstract

According to the real-time non-tracking monitoring video remnant detection method provided by the invention, manual feature extraction and deep learning recognition are combined, and non-tracking remnant detection is realized. The method comprises the following steps: firstly, on the basis of a frame difference method, counting continuous frame sequence change conditions of a foreground region toobtain an initial static target region; Then, we will make Two kinds of manual design characteristics of a gradient direction straight directional diagram and hue- Saturation-lightness combineto carryout suspicious object pre-judgment, and a pseudo static target area caused by influences of illumination changes and the like is eliminated. And finally, known objects and pedestrians are excluded bycombining a deep learning technology, so that final confirmation is carried out on suspicious objects, and non-tracking detection of the remaining objects is realized. According to the method, pseudotargets generated by illumination change and pedestrian retention in a scene can be eliminated, remaining objects can be accurately detected, the single-frame processing time is shorter than that ofother two methods, and the requirement for real-time alarming can be met.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a real-time non-tracking surveillance video remnant detection method. Background technique [0002] Relics are objects that appear in the scene and stay in the scene for a certain period of time. In some public places (squares, airports, railway stations, etc.), by monitoring suspicious remnants and issuing alarms, the occurrence of public safety incidents can be effectively reduced, the personal and property safety of the public can be protected, and the long-term stability of the society can be maintained. Therefore, remnants detection plays a very important role in intelligent video surveillance system. Remnant object detection not only needs to distinguish between foreground objects and stationary foreground objects, but also to judge whether the stationary foreground objects are pedestrians and illuminated. The current methods mostly use the method of ...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06T7/90G06T5/40G06N3/04
CPCG06T5/40G06T7/0002G06T7/90G06T2207/10016G06N3/045
Inventor 方贤勇杨振青汪粼波李薛剑
Owner ANHUI UNIVERSITY
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