Adaptive Object Detection Using Dual-Stream Resolution
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Solution Overview
Problem
Current object detection and recognition systems are time and resource intensive, particularly when performing face detection and recognition, as they often require high-resolution images for accurate identification, leading to increased latency and power consumption.
Innovation Solution
The system employs a dual-stream approach with a low-resolution stream for object detection and a high-resolution stream for object recognition, where the resolution of each stream is dynamically adjusted based on feedback to balance latency, quality, and power consumption, using synchronized video frames to maintain performance across varying conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution images are used for object detection and recognition, then recognition accuracy is improved, but processing time and power consumption increase
Solution Approach 1:
The system segments the image processing task into two distinct streams: a low-resolution stream for object detection and a high-resolution stream for object recognition. This segmentation allows each stream to operate at its optimal resolution, improving overall system efficiency while maintaining accuracy.
Solution Approach 2:
The system applies different quality levels to different parts of the processing pipeline. Low-resolution images are used for the initial detection phase where high detail is not critical, while high-resolution images are reserved for the recognition phase where detailed analysis is required. This local quality differentiation reduces overall processing time and power consumption.
2Measurement precision
If high-resolution images are used for object detection and recognition, then recognition accuracy is improved, but power consumption increases
Solution Approach 1:
The processing workload is segmented across two resolution levels, reducing the total computational energy required. The low-resolution stream handles the energy-intensive detection task, while the high-resolution stream processes only the smaller subset of images that require detailed analysis for recognition.
Solution Approach 2:
The system applies high computational resources (high resolution) only where necessary for recognition, rather than uniformly across all processing stages. This localized application of high quality processing significantly reduces overall power consumption while maintaining recognition accuracy.
3Speed
If low-resolution stream is used for object detection, then processing speed is improved, but detection quality may deteriorate
Solution Approach 1:
The system segments the detection and recognition functions into separate streams with appropriate resolution levels. The low-resolution stream is optimized for speed in detection, while the high-resolution stream ensures quality for recognition, with feedback mechanisms to maintain overall detection quality.
Solution Approach 2:
The system uses feedback from the low-resolution detection stream to guide the high-resolution recognition stream. This feedback mechanism ensures that detection quality is maintained by using detection results to select and process only relevant high-resolution images for recognition.
Data Source
AI summary
Techniques and systems are provided for performing object recognition for video frames. For example, one or more video frames of a first video are obtained. The one or more video frames have a first resolution and include one or more objects. The one or more objects are detected in the one or more video frames of the first video based on object detection being performed on the one or more video frames. One or more video frames of a second video are also obtained. The one or more video frames of the second video also include the one or more objects and have a second resolution that is greater than the first resolution. An object recognition process is performed on the one or more video frames of the second video to recognize or identify the one or more objects detected in the one or more video frames of the first video. In some cases, feedback associated with the object detection and/or the object recognition process can be obtained, and a resolution of the first video and/or a resolution of the second video can be adjusted based on the feedback.