Aiming at the problem of large feature reconstruction error and local constraint loss between features in fast low rank coding algorithm, an enhanced local constraint fast low rank coding algorithm was put forward. Firstly, the clustering algorithm was used to cluster the features in the image, and obtain the local similarity feature set and the corresponding clustering center. Secondly, the K visual words were found by using the K Nearest Neighbor (KNN) strategy in the visual dictionary, and then the K visual words were combined into the corresponding visual dictionary. Finally, the corresponding feature code of the local similarity feature set was obtained by using the fast low rank coding algorithm. On Scene-15 and Caltech-101 image datasets, the classification accuracy of the modified algorithm was improved by 4% to 8% compared with the original fast low rank coding algorithm, and the coding efficiency was improved by 5 to 6 times compared with sparse coding. The experimental results demonstrate that the modified algorithm can make local similarity features have similar codes, so as to express the image content more accurately, and improve the classification accuracy and coding efficiency.

Aiming at such interferences as false detection and missed detection which can't be overcome by the existing virtual loop detection algorithm in multilane traffic flow detection, a novel traffic flow detection algorithm based on adaptive virtual loop was put forward. According to the image binarization principle, quadratic estimation was adopted in the foreground detection part of the Visual Background extractor (ViBe) algorithm, and the background updating mechanism was changed. A new improved ViBe algorithm was presented to achieve the purposes of rapidly eliminating the ghost and completing the foreground object extraction. Then, the fixed detection area was set on the road, and the mobile virtual loop was established or canceled according to the moving target trajectory of fixed detection area. The traffic flow algorithm based on virtual loop was further used to achieve traffic flow statistics. Three different scenarios:no vehicle lane change with 4 lanes, vehicle lane change with 2 lanes, vehicle lane change with 3 lanes and sudden environmental change, were chosen for experiments and the traffic flow detection accuracy of the proposed algorithm was 8.9, 25 and 16.6 percentage points higher than that of the traditional virtual loop detection algorithm. The experimental results show that the proposed algorithm is more suitable for multilane traffic flow detection.