Dash Cam Frame Buffer Partitioning for Real-Time Object Detection
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Solution Overview
Problem
Dash camera systems face inefficiencies in processing video frames due to capturing unnecessary vehicle hood and sky areas, leading to increased processing load and reduced resource availability for detecting objects of interest.
Innovation Solution
Implementing a dynamic partitioning of the input frame buffer to optimize resource usage by ignoring static vehicle hood regions and adjusting processing based on geolocation and object detection, allowing for efficient analysis and metadata extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of moving object
If the dash camera captures the entire video frame including vehicle hood and sky areas, then the field of view is maximized, but the processing load increases and resource availability for detecting objects of interest decreases
Solution Approach 1:
The video frame buffer is divided into multiple regions including a hood region, a sky region, and a region of interest. By segmenting the frame buffer, the system can apply different processing strategies to different regions, performing full object detection only in the region of interest while using simplified processing or skipping processing in the hood and sky regions, thereby reducing overall processing load while maintaining comprehensive field of view capture
Solution Approach 2:
Different processing quality levels are applied to different regions of the video frame. The region of interest receives high-quality processing with full object detection algorithms, while the hood and sky regions receive reduced or no processing. This local quality differentiation optimizes resource allocation by concentrating computational resources where they are most needed for detecting objects of interest
2Reliability
If the dash camera processes all regions of the video frame with equal processing intensity, then comprehensive object detection is achieved, but processing time increases and real-time metadata extraction becomes difficult
Solution Approach 1:
The frame buffer is segmented into regions with different processing priorities. The region of interest is processed with full detection algorithms to ensure high reliability, while the hood and sky regions are either processed with simplified algorithms or skipped entirely. This segmentation enables the system to maintain high object detection accuracy in critical areas while reducing overall processing time to meet real-time requirements
Solution Approach 2:
Instead of applying full processing intensity to the entire frame, the system applies partial processing only to the region of interest where objects of interest are likely to be located. The hood and sky regions undergo minimal or no processing, allowing the system to achieve sufficient detection accuracy for critical areas within the time constraints of real-time processing
3Quantity of substance
If the system allocates buffer memory for the entire video frame, then complete video data is available for analysis, but memory resources are wasted on storing unnecessary hood and sky regions
Solution Approach 1:
The system extracts and isolates the region of interest from the complete video frame buffer. By identifying and separating the relevant portion of the frame where objects of interest are likely to be located, the system can allocate buffer memory resources more efficiently, storing and processing only the necessary data while discarding or minimizing storage of the hood and sky regions that consume memory without providing detection value
Data Source
AI summary
An apparatus comprising a processor and a memory. The processor may be configured to (i) receive video frames captured by a capture device and (ii) perform video analysis on the video frames to extract metadata corresponding to objects detected in the video frames. The memory may be configured to (i) store co-ordinates corresponding to a plurality of regions of the video frames and (ii) provide temporary storage for the video frames to enable the processor to perform the video analysis. The video analysis may perform a different amount of processing for detecting the objects on each of the plurality of regions. One of the regions corresponds to a portion of the video frame comprising a hood of a vehicle.