Jiahao Nie

Hangzhou Dianzi University

Accurate estimating the state-of-charge (SOC) of Li-ion battery contributes significantly to electric vehicle safety. Existing methods typically focus on the traditional recurrent neural networks to encode time series features for SOC estimation. However, these methods rely solely on their own structure to extract time series correlated features, i...

The advancement of deep transfer learning has motivated research into the realization of intelligent fault diagnosis schemes for rolling bearing. Nevertheless, existing research rarely provides further insight into the importance of statistical distance metric-based methods and adversarial learning-based methods in domain adaptation, and the common...

State-of-X (SOX) estimation of lithium-ion batteries is crucial for safe operation of electric vehicles (EVs). However, EVs have long suffered from complex and variable operation conditions. While deep learning-based state estimation demonstrates strong generalization to such operation conditions, it typically focus on estimating a single state, an...

3D single object tracking plays a crucial role in numerous applications such as autonomous driving. Recent trackers based on motion-centric paradigm perform well as they exploit motion cues to infer target relative motion across successive frames, which effectively overcome significant appearance variations of targets and distractors caused by occl...

The mixing process of battery production contains a variety of monitoring sensors. These sensors generate a large number of multivariate time series during production, which reflect the potential operating conditions of the production equipment. By accurately detecting anomalies in the equipment, battery quality can be improved. However, due to the...

With the rapid growth of the electric vehicle industry, the demand for battery fault detection methods is also growing. Effective battery defect detection methods help maintain the performance of the battery pack. In this research, a reconstruction-based model for internal short circuit (ISC) detection in battery packs is presented by combining tra...

Two-stage point-to-box network acts as a critical role in the recent popular 3D Siamese tracking paradigm, which first generates proposals and then predicts corresponding proposal-wise scores. However, such a network suffers from tedious hyper-parameter tuning and task misalignment, limiting the tracking performance. Towards these concerns, we prop...

Current 3D single object tracking methods are typically based on VoteNet, a 3D region proposal network. Despite the success, using a single seed point feature as the cue for offset learning in VoteNet prevents high-quality 3D proposals from being generated. Moreover, seed points with different importance are treated equally in the voting process, a...

Two-stage point-to-box network acts as a critical role in the recent popular 3D Siamese tracking paradigm, which first generates proposals and then predicts corresponding proposal-wise scores. However, such a network suffers from tedious hyper-parameter tuning and task misalignment, limiting the tracking performance. Towards these concerns, we prop...

Siamese trackers based on 3D region proposal network (RPN) have shown remarkable success with deep Hough voting. However, using a single seed point feature as the cue for voting fails to produce high-quality 3D proposals. Additionally, the equal treatment of seed points in the voting process, regardless of their significance, exacerbates this limit...

Existing multi-stage trackers treat visual object tracking as a multiple feature extraction and similarity metric process. However, the similarity metric methods used in them are typically based on linear cross-correlation, ignoring the matching of detailed information. Moreover, the feature extraction operators (e.g., RoI align) lead to a sub-opti...

One-shot multiple object tracking (MOT), which learns object detection and identity embedding in a unified network, has attracted increasing attention due to its low complexity and high tracking speed. However, most one-shot trackers ignore that detection and re-identification (ReID) require different representations of features. The inherent diffe...

Current 3D single object tracking methods are typically based on VoteNet, a 3D region proposal network. Despite the success , using a single seed point feature as the cue for offset learning in VoteNet prevents high-quality 3D proposals from being generated. Moreover, seed points with different importance are treated equally in the voting process,...

Three-dimensional multimodality multi-object tracking has attracted great attention due to the use of complementary information. However, such a framework generally adopts a one-stage association approach, which fails to perform precise matching between detections and tracklets, and, thus, cannot robustly track objects in complex scenes. To address...

With the widespread use of deep learning in single object tracking task, mainstream tracking algorithms treat tracking as a combined classification and regression problem. Classification aims at locating an arbitrary target, and regression aims at estimating the corresponding bounding box. In this paper, we focus on regression and propose a novel b...

Existing multi-object trackers mainly apply the tracking-by-detection (TBD) paradigm and have achieved remarkable success. However, the mainstream methods execute their detection networks alone, without taking full advantage of the information derived from tracking so that the detection and tracking processes can benefit from each other. In this pa...

Multiple object tracking (MOT) in unmanned aerial vehicle (UAV) videos is a fundamental task and can be applied in many fields. MOT consists of two critical procedures, i.e., object detection and re-identification (ReID). One-shot MOT, which incorporates detection and ReID in a unified network, has gained attention due to its fast inference speed....

The application of Siamese network in visual object tracking has greatly improved the performance of the tracker recently, which can take both accuracy and speed into account. However, the accuracy of Siamese network tracker is limited to a great extent. In order to solve the above problems, a key information feature perception module based on chan...

Siamese tracking paradigm has achieved great success, providing effective appearance discrimination and size estimation by the classification and regression. While such a paradigm typically optimizes the classification and regression independently, leading to task misalignment (accurate prediction boxes have no high target confidence scores). In th...

Siamese tracking paradigm has achieved great success, providing effective appearance discrimination and size estimation by classification and regression. While such a paradigm typically optimizes the classification and regression independently, leading to task misalignment (accurate prediction boxes have no high target confidence scores). In this p...