Research On High-speed Camera Data Retrieval Technology Based On SDN Network

In the testing of new equipment performance in the shooting range,high-speed cam eras are needed to record high-speed videos of the test targets,which results in a large amount of high-speed video data.The traditional network architecture has become increa singly bloated and cannot meet the transmission demands of massive video data.Meanw hile,there are many redundant information in video data,and how to quickly identify t he key information of testing activities in massive video data is still a major challenge.To address the above two issues,thesis takes high-speed video data as the research ob ject and conducts research on the fast transmission and accurate retrieval of high-speed video data under the new network architecture in the following aspects.1、In order to address the potential problems of inefficient data transmission and n etwork congestion faced by traditional networks when dealing with large data flows,this article introduces a new type of network,Software Defined Network(SDN),to optimize video transmission networks.The core idea of SDN,which separates forwarding and control,is utilized,and the Ryu controller and STP protocol are combined to address the p roblem of loops in the network,there by improving network utilization.Experimental res ults show that compared to traditional networks,SDN networks have better throughput f or video data,lower packet loss rates,and lower latency.These improvements enhance t he performance of video transmission networks,making video data transmission more ef ficient.2、For unstructured video data,a multi-feature fusion and adaptive dual-threshold shot boundary detection algorithm is designed.First,the image is segmented using the idea of image block to extract the HSV color feature,LBP texture feature,and Hu invariant moment shape feature of each block of the image.Then,the feature vectors of each block are fused and the feature of the entire image is represented by adding them up according to the weight coefficient.Finally,adaptive high and low thresholds are determined to detect sudden and gradual shot boundary changes in the video.Experimental results show that the improved algorithm can accurately detect changes in shot boundaries and reduce false detection caused by environmental interference,thereby improving the accuracy of shot boundary detection.3.In the field of keyframe extraction,an advanced technique based on an improved k-means clustering algorithm is proposed.This approach utilizes the Affinity Propagation(AP)algorithm to determine the upper bound of the clustering number,which significantly narrows down the range of clustering.Additionally,this method incorporates the idea of density blocks to select the initial cluster centers based on the shortest distance between samples in each density interval.The selection of stable cluster centers is achieved through iterative calculations,which guarantees the uniqueness of the selected centroids.The improved algorithm can automatically adjust the number of keyframes based on the content of the video,and the clustering results are more stable.As a result,the extracted keyframes are more accurate,representative,and effectively reduce the redundancy of video frames.This technique has been evaluated with high standards and demonstrated its effectiveness in scientific research.Finally,experimental analysis and verification were conducted on the proposed SDN-based video transmission network and content-based video retrieval technology.The experimental results show that the network quality has been greatly improved compared to traditional networks,and the accuracy of video shot boundary detection and keyframe extraction indicates that the improved algorithms are more accurate and representative than traditional methods.Moreover,the video retrieval results show a higher accuracy compared to single-feature retrieval.