Vehicle Re-identification in Still Images: Application of Semi-supervised Learning and Re-ranking

Vehicle re-identification (re-ID), namely, finding exactly the same vehicle from a large number of vehicle images, remains a great challenge in computer vision. Most existing vehicle re-ID approaches follow a fully supervised learning methodology, in which sufficient labeled training data is required. However, this limits their scalability to realistic applications, due to the high cost of data labeling. In this paper, we adopted a Generative Adversarial Network (GAN) to generate unlabeled samples and enlarge the training set. A semi supervised learning scheme with the Convolutional Neural Networks (CNN) was proposed accordingly, which assigns a uniform label distribution to the unlabeled images to regularize the supervised model and improve the performance of the vehicle re-ID system. Besides, an improved re-ranking method based on the Jaccard distance and k-reciprocal nearest neighbors is proposed to optimize the initial rank list. Extensive experiments over the benchmark datasets VeR1-776, VehicleID and VehicleReID have demonstrated that the proposed method outperforms the state-of-the-art approaches for vehicle re-ID

LiPar: A Lightweight Parallel Learning Model for Practical In-Vehicle Network Intrusion Detection

With the development of intelligent transportation systems, vehicles are exposed to a complex network environment. As the main network of in-vehicle networks, the controller area network (CAN) has many potential security hazards, resulting in higher requirements for intrusion detection systems to ensure safety. Among intrusion detection technologies, methods based on deep learning work best without prior expert knowledge. However, they all have a large model size and rely on cloud computing, and are therefore not suitable to be installed on the in-vehicle network. Therefore, we propose a lightweight parallel neural network structure, LiPar, to allocate task loads to multiple electronic control units (ECU). The LiPar model consists of multi-dimensional branch convolution networks, spatial and temporal feature fusion learning, and a resource adaptation algorithm. Through experiments, we prove that LiPar has great detection performance, running efficiency, and lightweight model size, which can be well adapted to the in-vehicle environment practically and protect the in-vehicle CAN bus security.Comment: 13 pages, 13 figures, 6 tables, 51 referenc

Clay mineralogy indicates a mildly warm and humid living environment for the Miocene hominoid from the Zhaotong Basin, Yunnan, China

Global and regional environmental changes have influenced the evolutionary processes of hominoid primates, particularly during the Miocene. Recently, a new Lufengpithecus cf. lufengensis hominoid fossil with a late Miocene age of ~6.2 Ma was discovered in the Shuitangba (STB) section of the Zhaotong Basin in Yunnan on the southeast margin of the Tibetan Plateau. To understand the relationship between paleoclimate and hominoid evolution, we have studied sedimentary, clay mineralogy and geochemical proxies for the late Miocene STB section (~16 m thick; ca. 6.7–6.0 Ma). Our results show that Lufengpithecus cf. lufengensis lived in a mildly warm and humid climate in a lacustrine or swamp environment. Comparing mid to late Miocene records from hominoid sites in Yunnan, Siwalik in Pakistan, and tropical Africa we find that ecological shifts from forest to grassland in Siwalik are much later than in tropical Africa, consistent with the disappearance of hominoid fossils. However, no significant vegetation changes are found in Yunnan during the late Miocene, which we suggest is the result of uplift of the Tibetan plateau combined with the Asian monsoon geographically and climatically isolating these regions. The resultant warm and humid conditions in southeastern China offered an important refuge for Miocene hominoids