To effectively extract the temporal information between consecutive video frames， a prediction network IndRNN-VAE （Independently Recurrent Neural Network-Variational AutoEncoder） that fuses Independently Recurrent Neural Network （IndRNN） and Variational AutoEncoder （VAE） network was proposed. Firstly， the spatial information of video frames was extracted through VAE network， and the latent features of video frames were obtained by a linear transformation. Secondly， the latent features were used as the input of IndRNN to obtain the temporal information of the sequence of video frames. Finally， the obtained latent features and temporal information were fused through residual block and input to the decoding network to generate the prediction frame. By testing on UCSD Ped1， UCSD Ped2 and Avenue public datasets， experimental results show that compared with the existing anomaly detection methods， the method based on IndRNN-VAE has the performance significantly improved， and has the Area Under Curve （AUC） values reached 84.3%， 96.2%， and 86.6% respectively， the Equal Error Rate （EER） values reached 22.7%， 8.8%， and 19.0% respectively， the difference values in the mean anomaly scores reached 0.263， 0.497， and 0.293 respectively. Besides， the running speed of this method reaches 28 FPS （Frames Per Socond）.

Concerning that the traditional image retrieval methods are confronted with massive image data processing problems, a new solution for large-scale image retrieval, named MR-BoVW, was proposed, which was based on the traditional Bag of Visual Words (BVW) approach and MapReduce model to take advantage of the massive storage capacity and powerful parallel computing ability of Hadoop. To handle image data well, firstly an improved method for Hadoop image processing was introduced, and then, the MapReduce layout was divided into three stages: feature vector generation, feature clustering, image representation and inverted index construction. The experimental results demonstrate that the MR-BoVW solution shows good performance on speedup, scaleup, and sizeup. In fact, the efficiency results are all greater than 0.62, and the curve of scaleup and sizeup is gentle. Thus it is suitable for large-scale image retrieval.

The available Visual Background extractor (ViBe) only uses the spatial information of pixels to build background model ignoring the time information,as a result to make the accuracy of detection decrease. In addition, the detection radius and random sampling factor of updating background model are fixed parameters, the effect of detection is not ideal on the circumstances of dynamic background interference and camera shake. In order to solve these problems, an adaptive moving target detection method based on spatial-temporal background model was proposed. Firstly, the time information was added to ViBe to set up spatial-temporal background model. And then the complexity of the background was reflected by the standard deviation of the samples in the background model. So the standard deviation was able to change the detection radius and random sampling factor of updating background model to adapt to the change of background. The experimental results indicate that the proposed method can not only effectively detect the foreground with static background and uniformity of light, but also have certain inhibitory effects in the cases of the light changing greatly, camera shaking, and the dynamic background interference, and so on. It is capable of improving the precision of detection.