# An Image-Retrieval Method Based on Cross-Hardware Platform Features
## 3 Methods
### Image Retrieval Based on Cross-Hardware Platform Features  
The quality of the feature will affect the image-retrieval results based on features. Data-structuring technology is mainly used to extract structural features and a build feature database. Due to the differences in hardware platforms, we have implemented different deployment schemes for data structuring. The deployment schemes based on the Tesla T4 and Atlas 300V is shown in Figure 2. For video streaming, the deployment scheme based on the Tesla T4 first uses a hardware acceleration unit to decode the streaming media, then down-samples the video frame, then performs target detection and tracking, selects the best quality targets to capture and perform attribute analysis and feature extraction, and finally encodes the captured images and stores the analysis results and captured images into the database. However, due to memory limitations and optimal performance, the deployment schemes based on the Atlas 300V first need to send the streaming media from the host to the device through PCIe, then use the hardware acceleration unit to decode the streaming media, then down-sample the video frame, then perform target detection and tracking, then transmit the video frame encoding to the host, and select the best quality target to capture. Then, the captured image is transmitted to the device for decoding, attribute analysis, and feature extraction, and finally the analysis results and captured images are stored in the database. For images, the deployment schemes based on the Tesla T4 first decode them using a hardware acceleration unit, then perform object detection, attribute analysis, and feature extraction, and finally stores the results into a database. In addition, the Atlas 300V-based deployment scheme first sends the image from the host to the device via PCIe, then decodes the image using a hardware acceleration unit, then performs object detection, attribute analysis, and feature extraction, and finally sends the results back from the device to the host and stores them in the database. Host refers to the X86 server and ARM server connected to the device, which uses the neural network (NN) computing capability provided by the device to complete services. A device is a hardware device installed with an Ascend AI processor and connected to a host through a PCIe port to provide NN computing capabilities [54, 55]. The YUV format has a small amount of data and strong anti-noise, which is conducive to image compression and transmission and reduces bandwidth consumption [56]. According to the chip whitepaper, the Tesla T4 is in YUV420P format [7, 8] and the Atlas 300V is in YUV420 SP format [17, 54]. Since the image is encoded by the Joint Photographic Experts Group (JPEG), in order to reduce the feature difference caused by image quality loss, we set the image coding quality factor to 95. Video structuring is a technology for extracting video content information. It organizes video content into structured information that can be understood by computers and people  
Figure 2: The deployment schemes based on the Tesla T4 and Atlas 300V. and semi-structured information that can be compared according to semantic relationships by means of spatiotemporal segmentation, feature extraction, and object recognition. Video structuring can capture and extract structured information of motor vehicles, license plates, non-motor vehicles, and other information in video-streaming media in real time, aiming at efficient extraction and structured storage of massive video information within a certain spatiotemporal range, greatly reducing the spatiotemporal range of retrieval, achieving structured data research and judgment of specific targets or large amounts of data, and improving storage and retrieval efficiency. It has high practical value for target trajectory research, target quick search, traffic-situation analysis, privacy protection and other directions.