Real-Time Garbage Dumping Detection Using Joint-Object Distance Analysis
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Solution Overview
Problem
Existing video surveillance systems face challenges in detecting garbage dumping actions in real-time due to high false detection rates and limitations in identifying objects that are thrown away, especially when objects are obstructed or have varying shapes, and current methods struggle to differentiate between abandoned objects and garbage dumping events.
Innovation Solution
A method and apparatus that detect garbage dumping actions by analyzing changes in distance between joint coordinates and objects in images, using joint information to determine if an object has been dumped, and incorporating a voting model to reduce false detections, along with additional methods considering the person's pose and potential dumping regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional foreground extraction technique is used to detect abandoned objects, then detection can be performed, but false detections occur (e.g., parked vehicles detected as abandoned objects)
Solution Approach 1:
The detection process is segmented into multiple independent analysis dimensions: motion detection identifies moving objects, object recognition identifies what the object is, and human behavior analysis determines whether the object was abandoned by a person. This segmentation allows each component to focus on its specific task and reduces false detections by requiring consensus across multiple segments.
Solution Approach 2:
Human behavior analysis acts as an intermediary layer between object detection and final abandonment determination. This intermediary analyzes the temporal and spatial relationship between persons and objects, determining whether a person actually abandoned the object or simply passed by it, thereby filtering out false detections like parked vehicles.
2Difficulty of detecting and measuring
If deep learning-based object detectors are used, then object detection capability is improved, but it is still not easy to define and detect objects that are thrown away due to their wide variety of shapes
Solution Approach 1:
The invention extracts and focuses specifically on the hand-object interaction region from the entire image. By extracting only the relevant region where hands contact objects during abandonment, the system simplifies the detection task and avoids the need to detect all possible object shapes throughout the entire scene, thereby improving both detection capability and adaptability.
Solution Approach 2:
The system transitions from detecting objects in the traditional spatial dimensions to analyzing the temporal dimension of hand-object interaction. By examining the sequence of hand movements and object position changes over time, the system can detect abandonment behavior regardless of object shape, adding a temporal dimension to the detection process.
3Reliability
If post-processing method is used for image analysis, then abandoned objects can be detected, but it is impossible to detect garbage dumping action immediately when the event occurs
Solution Approach 1:
The system performs preliminary detection of hand-object interaction and human behavior patterns in real-time as the abandonment action is occurring, rather than waiting for post-processing. By preliminarily analyzing motion patterns, hand positions, and temporal sequences during the event itself, the system can detect and respond to garbage dumping actions immediately as they happen.
4Device complexity
If a single detection method is used, then the system is simple, but false detection rate is high and accuracy is insufficient
Solution Approach 1:
The invention merges multiple detection methods into a unified system: motion detection, object recognition, and human behavior analysis are combined and work together. The system integrates these different detection approaches, allowing them to complement each other and validate results through mutual confirmation, thereby improving reliability while maintaining manageable complexity through systematic integration.
Data Source
AI summary
A method and apparatus for detecting a garbage dumping action in real time on a video surveillance system are provided. A change region, which is a motion region, from an input image is detected, joint information including joint coordinates corresponding to a region in which joints exist is generated, and an object held by a person from the image using the change region and the joint information is detected. Then, an action of dumping the object based on a distance between the object and the joint coordinates is detected.