Warehouse Risk Mapping Using Video Sensors for Route Optimization
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Solution Overview
Problem
Warehouse environments face inefficiencies in order fulfillment due to dynamic operational conditions and human performance variability during pallet handling and order-picking processes, necessitating improved risk management and route optimization.
Innovation Solution
A system utilizing video sensors to capture video streams, generate risk maps, and identify risk zones within the warehouse environment, including risk types and levels, which are then used to optimize operator routes and update in real-time, enabling better operational management and incident detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If video sensors and real-time risk mapping are implemented, then safety and risk management are improved, but device complexity and implementation cost increase
Solution Approach 1:
The system uses existing video sensors from the surveillance system for multiple purposes: both security monitoring and risk identification. The same video streams are processed to generate both security alerts and risk maps, eliminating the need for dedicated sensors and reducing overall system complexity.
Solution Approach 2:
A centralized processing unit acts as an intermediary that receives video streams from existing sensors and generates both security information and risk identification information. This mediator coordinates between the existing surveillance infrastructure and the new risk mapping functionality, simplifying integration.
2Measurement precision
If comprehensive video monitoring and risk analysis are implemented, then measurement precision of risk zones is improved, but use of energy and computational resources increase
Solution Approach 1:
The system processes video streams at different levels of detail based on the specific risk assessment needs. Not all zones require the same level of analysis intensity, allowing the system to allocate computational resources efficiently while maintaining adequate measurement precision where needed.
Solution Approach 2:
The system pre-processes video streams to extract key features and characteristics before performing detailed risk analysis. By preparing data in advance and identifying potential risk areas proactively, the system reduces the computational burden during real-time processing and lowers energy consumption.
Data Source
AI summary
A system and method for identifying risk in warehouse environments includes video sensors configured to capture video streams and a central processing unit communicatively coupled to video sensors. The central processing unit is configured with an emerging risk discovery unit configured to detect a current risk subject in the obtained plurality of real time video frames. Further, the plurality of real time video frames are stored in a memory. A location of the current risk subject detected in the obtained plurality of real time video frames is detected. Further, physical characteristics of current risk subject for predicting one or more actions performed by the current risk subject are estimated, and actions and location of the current risk subject are processed to detect patterns or movements and activities undertaken by one or more risk subjects.