Vehicle Make and Model Recognition using Bag of Expressions Vehicle make and model recognition (VMMR) is a key task for automated vehicular surveillance (AVS) and various intelligent transport system (ITS) applications. In this paper, we propose and study the suitability of the bag of expressions (BoE) approach for VMMRbased applications. The method includes neighborhood information in addition to visual words. BoE improves the existing power of a bag of words (BOW) approach, including occlusion handling, scale invariance and view independence. The proposed approach extracts features using a combination of different keypoint detectors and a Histogram of Oriented Gradients (HOG) descriptor. An optimized dictionary of expressions is formed using visual words acquired through kmeans clustering. The histogram of expressions is created by computing the occurrences of each expression in the image. For classification, multiclass linear support vector machines (SVM) are trained over the BoEbased features representation. The approach has been evaluated by applying crossvalidation tests on the publicly available National Taiwan Ocean UniversityMake and Model Recognition (NTOUMMR) dataset, and experimental results show that it outperforms recent approaches for VMMR. With multiclass linear SVM classification, promising average accuracy and processing speed are obtained using a combination of keypoint detectors with HOGbased BoE description, making it applicable to realtime VMMR systems.

Pose Recognition for Dense Vehicles under Complex Street Scenario Locating vehicles in the surrounding environment and distinguishing their poses are crucial in autonomous driving systems. An intelligent transportation system expects a visionbased object detector not only to detect and classify vehicles but also to recognize the poses of them in estimating their future movement direction. However, most of the current object detection algorithms only classify different classes of vehicles; and there are rare studies on classifying different poses within the same class of vehicles. In addition, the currently available datasets have no annotation data that are specific to different poses of intraclass vehicles. To achieve the vehicles pose recognition, we build a vehicle pose dataset based on the Cityscapes dataset. Furthermore, we also improve the network model of the YOLO v2 detector to support simultaneous vehicle detection and pose classification with an improvement of 10.71% mean average precision (mAP) comparing to the default network which runs in realtime with 29 FPS.