Driving behavior plays a key role in the interaction between vehicle and driver in transportation systems. Some applications about driving behavior in Advanced Driver Assistance Systems (ADAS) improve driving safety significantly. This paper introduces the driving context and models driving behavior in a combination of cognitive perspective and data-driven perspective. First, we use a cognitive fusion method by adding a delay time module to fuse the environmental information and inside information. To better capture the driving context relationship between outside and inside features, we transfer the behavior prediction task to the sequence labeling task by introducing the visual inertia hypothesis. We propose the Predictive-Bi-LSTM-CRF algorithm which used the Bidirectional Long-Short Term Memory Networks (Bi-LSTM) and Conditional Random Field (CRF) as the loss layer to model the driving behavior. Besides, we define a new comprehensive evaluation metric for the prediction task considering F1-score and the prediction time before maneuver together. Our experiment results achieve the state of art performance on the Brain4Cars dataset and demonstrate the applicability of our theory.

Weakly-supervised semantic segmentation under image tags supervision is a challenging task as it directly associates high-level semantic to low-level appearance. To bridge this gap, in this paper, we propose an iterative bottom-up and top-down framework which alternatively expands object regions and optimizes segmentation network. We start from initial localization produced by classification networks. While classification networks are only responsive to small and coarse discriminative object regions, we argue that, these regions contain significant common features about objects. So in the bottom-up step, we mine common object features from the initial localization and expand object regions with the mined features. To supplement non-discriminative regions, saliency maps are then considered under Bayesian framework to refine the object regions. Then in the top-down step, the refined object regions are used as supervision to train the segmentation network and to predict object masks. These object masks provide more accurate localization and contain more regions of object. Further, we take these object masks as initial localization and mine common object features from them. These processes are conducted iteratively to progressively produce fine object masks and optimize segmentation networks. Experimental results on Pascal VOC 2012 dataset demonstrate that the proposed method outperforms previous state-of-the-art methods by a large margin.

Due to the relatively fixed shape and color, head detection is widely used in many computer vision tasks, such as finding people and crowd counting. In this paper, we propose a method to detect human heads including: (1) A pairwise CNN model based on key parts context of human head and shoulder. (2) A fusion algorithm by using the priority of scene geometry structure. (3) To further test our approach, we collect a dataset about the passengers inside the bus with head annotations. This dataset composed of 2316 representative images extracted from a total of twenty hours of video. We evaluate our method on two indoor human head datasets and achieve state-of-the-art performance.

Region proposals followed by a classification network is a famous structure in object detection task. Recently, there are a lot of proposal methods that work well via enough samplings. However, how to use as few samples as possible to achieve high recall performance is still a difficult problem. In this paper, we propose a novel region proposal method via fusion features for general object detection. By establishing an encoding of object attributes, an objectness candidate inference method based on energy function is proposed for region proposal ranking, followed by a multi-level non maximum suppression strategy for further adjusting. Experiment on PASCAL VOC shows that our method achieves better performance on region proposal and object detection tasks.

In this paper, we propose a novel edge preserving and multi-scale contextual neural network for salient object detection. The proposed framework is aiming to address two limits of the existing CNN based methods. First, region-based CNN methods lack sufficient context to accurately locate salient object since they deal with each region independently. Second, pixel-based CNN methods suffer from blurry boundaries due to the presence of convolutional and pooling layers. Motivated by these, we first propose an end-to-end edge-preserved neural network based on Fast R-CNN framework (named RegionNet) to efficiently generate saliency map with sharp object boundaries. Later, to further improve it, multi-scale spatial context is attached to RegionNet to consider the relationship between regions and the global scenes. Furthermore, our method can be generally applied to RGB-D saliency detection by depth refinement. The proposed framework achieves both clear detection boundary and multi-scale contextual robustness simultaneously for the first time, and thus achieves an optimized performance. Experiments on six RGB and two RGB-D benchmark datasets demonstrate that the proposed method achieves state-of-the-art performance.

Traffic light recognition is one of the important tasks in the studies of intelligent transport system. In this paper, a robust traffic light recognition model based on vision information is introduced for on-vehicle camera applications. Our contribution mainly includes three aspects. First, in order to reduce computational redundancy, the aspect ratio, area, location, and context of traffic lights are utilized as prior information, which establishes a task model for traffic light recognition. Second, in order to improve the accuracy, we propose a series of improved methods based on an aggregate channel feature method, including modifying the channel feature for each types of traffic light and establishing a structure of fusion detectors. Third, we introduce a method of inter-frame information analysis, utilizing detection information of previous frame to modify original proposal regions, which makes the accuracy further improved. In the comparison of other traffic light detection algorithms, our model achieves competitive results on the complex scene VIVA data set. Furthermore, an analysis of small target luminous object detection tasks is given.

Recent advances in salient object detection have exploited the deep Convolutional Neural Network (CNN) to represent high-level semantic, however, due to the presence of convolutional and pooling layers, it is difficult for CNN to generate saliency map with sharp boundaries. In this paper, we propose multi-scale mask-based Fast R-CNN framework which generate saliency score of each region. Since the regions are segmented using edge-preserved methods, the results are naturally with sharp boundaries. To consider context information, we also propose low-level contrast and backgroundness prior which are complementary with high-level semantic. Finally, an edge-based propagation method which takes advantages of edge information is proposed to refine the saliency map. Experiments on three benchmark datasets demonstrate that the proposed method outperforms previous methods and achieves state-of-the-art performance.

Recent advances in object detection have exploited object proposals to speed up object searching. However, many of existing object proposal generators have strong localization bias or require computationally expensive diversification strategies. In this paper, we present an effective approach to address these issues. We first propose a simple and useful localization bias measure, called superpixel tightness. Based on the characteristics of superpixel tightness distribution, we propose an effective method, namely multi-thresholding straddling expansion (MTSE) to reduce localization bias via fast diversification. Our method is essentially a box refinement process, which is intuitive and beneficial, but seldom exploited before. The greatest benefit of our method is that it can be integrated into any existing model to achieve consistently high recall across various intersection over union thresholds. Experiments on PASCAL VOC dataset demonstrates that our approach improves numerous existing models significantly with little computational overhead.

In recent years, a variety of salient object detection methods under Bayesian framework have been proposed and many achieved state of the art. However, those ignore spatial relationships and thus background regions similar to the objects are also highlighted. In this paper, we propose a novel geodesic weighted Bayesian model to address this issue. We consider spatial relationships by attaching more importance to regions which are more likely to be parts of a salient object, thus suppressing background regions. First, we learn a combined similarity via multiple features to measure similarity of adjacent regions. Then, we apply the combined similarity as edge weight to construct an undirected weighted graph and compute geodesic distance. Last, we utilize the geodesic distance to weight the observation likelihood to infer a more precise saliency map. Experiments on several benchmark datasets demonstrate the effectiveness of our model.