The goal of video-based person re-identification is to recognize a person at different camera settings. Most previous methods use features from the full body to represent a person. In this paper, we propose a novel Spatial and Temporal Features Mixture Model (STFMM). Unlike previous approaches, our model first horizontally splits human body into N parts, which include the information of head, waist, legs and so on. The feature of each part is then integrated in order to achieve more expressive representation for each person. Experiments conducted on the iLIDS-VID and PRID-2011 datasets demonstrate that our approach outperforms the existing video-based person re-identification methods and significantly improves stability. Our model achieves a rank-1 CMC accuracy of 73.6 on the iLIDS-VID dataset and a rank-1 CMC accuracy of 47.8 for the cross-data testing.
Deep neural network (DNN) has achieved significant successes in computer vision, and the DNN-based methods have been studied and applied in person re-identification task @cite_27 @cite_16 @cite_24 @cite_14 @cite_19 @cite_23 @cite_4 @cite_32 . The DNN-based models are trained with a pair of inputs for learning a direct mapping from image or video sequence to feature space. Mclaughlin al @cite_24 combined CNN, RNN and Siamese network together, which is the first time applying DNN to the video-based re-identification. Attention mechanism @cite_31 @cite_18 has gained huge achievement in deep learning. Xu al @cite_19 proposed a joint Spatial and Temporal Attention Pooling Network (ASTPN) to extract sequence-level features by selecting informative frames and notable regions of each frame. Zhou al @cite_23 used Temporal Attention Model (TAM) to measure the importance of each frame in video sequence and applied Spatial Recurrent Model (SRM) to explore contextual information.
@cite_18 @cite_14 @cite_4 @cite_32 @cite_24 @cite_19 @cite_27 @cite_23 @cite_31 @cite_16
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Abstract Deep Neural Network (DNN) has recently achieved outstanding performance in a variety of computer vision tasks, including facial attribute classification. The great success of classifying facial attributes with DNN often relies on a massive amount of labelled data. However, in real-world applications, labelled data are only provided for some commonly used attributes (such as age, gender); whereas, unlabelled data are available for other attributes (such as attraction, hairline). To address the above problem, we propose a novel deep transfer neural network method based on multi-label learning for facial attribute classification, termed FMTNet, which consists of three sub-networks: the Face detection Network (FNet), the Multi-label learning Network (MNet) and the Transfer learning Network (TNet). Firstly, based on the Faster Region-based Convolutional Neural Network (Faster R-CNN), FNet is fine-tuned for face detection. Then, MNet is fine-tuned by FNet to predict multiple attributes with labelled data, where an effective loss weight scheme is developed to explicitly exploit the correlation between facial attributes based on attribute grouping. Finally, based on MNet, TNet is trained by taking advantage of unsupervised domain adaptation for unlabelled facial attribute classification. The three sub-networks are tightly coupled to perform effective facial attribute classification. A distinguishing characteristic of the proposed FMTNet method is that the three sub-networks (FNet, MNet and TNet) are constructed in a similar network structure. Extensive experimental results on challenging face datasets demonstrate the effectiveness of our proposed method compared with several state-of-the-art methods. Automatically detecting illustrations is needed for the target system.Deep Convolutional Neural Networks have been successful in computer vision tasks.DCNN with fine-tuning outperformed the other models including handcrafted features. Systems for aggregating illustrations require a function for automatically distinguishing illustrations from photographs as they crawl the network to collect images. A previous attempt to implement this functionality by designing basic features that were deemed useful for classification achieved an accuracy of only about 58 . On the other hand, deep neural networks had been successful in computer vision tasks, and convolutional neural networks (CNNs) had performed good at extracting such useful image features automatically. We evaluated alternative methods to implement this classification functionality with focus on deep neural networks. As the result of experiments, the method that fine-tuned deep convolutional neural network (DCNN) acquired 96.8 accuracy, outperforming the other models including the custom CNN models that were trained from scratch. We conclude that DCNN with fine-tuning is the best method for implementing a function for automatically distinguishing illustrations from photographs. We evaluated how deep convolutional neural networks (DCNN) could assist in the labor-intensive process of human visual searches for objects of interest in high-resolution imagery over large areas of the Earth’s surface. Various DCNN were trained and tested using fewer than 100 positive training examples (China only) from a worldwide surface-to-air-missile (SAM) site dataset. A ResNet-101 DCNN achieved a 98.2 average accuracy for the China SAM site data. The ResNet-101 DCNN was used to process ∼19.6 M image chips over a large study area in southeastern China. DCNN chip detections (∼9300) were postprocessed with a spatial clustering algorithm to produce a ranked list of ∼2100 candidate SAM site locations. The combination of DCNN processing and spatial clustering effectively reduced the search area by ∼660X (0.15 of the DCNN-processed land area). An efficient web interface was used to facilitate a rapid serial human review of the candidate SAM sites in the China study area. Four novice imagery analysts with no prior imagery analysis experience were able to complete a DCNN-assisted SAM site search in an average time of ∼42 min. This search was ∼81X faster than a traditional visual search over an equivalent land area of ∼88,640 km2 while achieving nearly identical statistical accuracy (∼90 F1). In existing convolutional neural networks (CNNs), both convolution and pooling are locally performed for image regions separately, no contextual dependencies between different image regions have been taken into consideration. Such dependencies represent useful spatial structure information in images. Whereas recurrent neural networks (RNNs) are designed for learning contextual dependencies among sequential data by using the recurrent (feedback) connections. In this work, we propose the convolutional recurrent neural network (C-RNN), which learns the spatial dependencies between image regions to enhance the discriminative power of image representation. The C-RNN is trained in an end-to-end manner from raw pixel images. CNN layers are firstly processed to generate middle level features. RNN layer is then learned to encode spatial dependencies. The C-RNN can learn better image representation, especially for images with obvious spatial contextual dependencies. Our method achieves competitive performance on ILSVRC 2012, SUN 397, and MIT indoor.