Distributed Sensor Nodes for Real-Time Object Detection
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Solution Overview
Problem
Current computer vision and AI systems in retail environments face challenges with high complexity and cost due to the need to process and synchronize large volumes of video streams and sensor data, leading to time delays, packet loss, and quality deterioration, making real-time object detection and identification inefficient.
Innovation Solution
A network of nodes with optical sensors and wireless communication capabilities, capable of contemporaneous object detection and identification, controls spatial orientation and focal length for improved image capture and processing, allowing for distributed processing without relying on centralized servers, using Bluetooth or Wi-Fi for communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If centralized server processing is used for video streams and sensor data, then comprehensive analysis capability is improved, but system complexity and processing time increase
Solution Approach 1:
The system divides the retail environment into multiple monitoring zones, each with its own sensor node that performs local detection and analysis. Each node independently processes data from its specific zone (shelves, aisles, or customer areas) using embedded machine learning models, eliminating the need to centralize all video streams and sensor data to a single server for basic monitoring tasks.
Solution Approach 2:
The patent transitions from traditional 2D video frame analysis to 3D spatial-aware analysis by integrating depth data from time-of-flight cameras and LiDAR sensors. This multi-dimensional approach enables more accurate object detection, customer behavior analysis, and shelf monitoring while distributing processing across multiple specialized nodes rather than one centralized system.
2Difficulty of detecting and measuring
If centralized server processing is used for video streams and sensor data, then comprehensive analysis capability is improved, but processing speed decreases
Solution Approach 1:
The system divides the retail environment into multiple monitoring zones, each with its own sensor node that performs local detection and analysis. Each node independently processes data from its specific zone (shelves, aisles, or customer areas) using embedded machine learning models, eliminating the need to centralize all video streams and sensor data to a single server for basic monitoring tasks.
Solution Approach 2:
Sensor nodes perform preliminary detection and filtering of data locally before transmitting to central servers. Edge computing capabilities enable nodes to pre-process video frames, detect objects of interest, and filter out irrelevant data, so only significant events and condensed information require centralized processing, dramatically reducing transmission time and server workload.
3Measurement precision
If large volumes of video streams are transmitted for processing, then detection accuracy is improved, but bandwidth requirements and operational costs increase
Solution Approach 1:
The system extracts only the essential and relevant features from video streams and sensor data at the edge devices before transmission. Sensor nodes perform local preprocessing to extract key parameters such as object detection results, customer behavior metrics, and shelf status information, transmitting only these condensed data points rather than raw video streams, thereby maintaining detection accuracy while minimizing bandwidth consumption.
4Reliability
If synchronous transmission of multiple video streams is implemented, then coordinated monitoring is improved, but time delays and asynchronization issues increase
Solution Approach 1:
Sensor nodes perform preliminary detection and filtering of data locally before transmitting to central servers. Edge computing capabilities enable nodes to pre-process video frames, detect objects of interest, and filter out irrelevant data, so only significant events and condensed information require centralized processing, dramatically reducing transmission time and server workload.
Solution Approach 2:
The system employs dynamic synchronization protocols that adapt to varying network conditions and event priorities. Instead of rigid synchronous transmission, nodes can operate semi-independently with local decision-making capabilities, allowing the system to maintain coordinated monitoring while tolerating minor transmission delays through event buffering and priority-based processing.
Data Source
AI summary
Systems and methods of detecting and identifying objects are provided. In one exemplary embodiment, a method performed by one of a plurality of network nodes, with each network node having an optical sensor and being operable to wirelessly communicate with at least one other network node, comprises sending, by a network node over a wireless communication channel, to another network node, an indication associated with an object that is detected and identified by the network node based on one more images of that object that are captured by the optical sensor of the network node. Further, the detection and identification of the object is contemporaneous with the capture of the one or more images of that object. Also, the network node is operable to control a spatial orientation of the sensor so that the sensor has a viewing angle towards the object.