Jie Nie

Bio: Jie Nie is an academic researcher from Ocean University of China. The author has contributed to research in topics: Computer science & Artificial intelligence. The author has an hindex of 7, co-authored 29 publications receiving 271 citations. Previous affiliations of Jie Nie include Tsinghua University.

A Review on Human Activity Recognition Using Vision-Based Method

Abstract: Human activity recognition (HAR) aims to recognize activities from a series of observations on the actions of subjects and the environmental conditions. The vision-based HAR research is the basis of many applications including video surveillance, health care, and human-computer interaction (HCI). This review highlights the advances of state-of-the-art activity recognition approaches, especially for the activity representation and classification methods. For the representation methods, we sort out a chronological research trajectory from global representations to local representations, and recent depth-based representations. For the classification methods, we conform to the categorization of template-based methods, discriminative models, and generative models and review several prevalent methods. Next, representative and available datasets are introduced. Aiming to provide an overview of those methods and a convenient way of comparing them, we classify existing literatures with a detailed taxonomy including representation and classification methods, as well as the datasets they used. Finally, we investigate the directions for future research.

Appearance Feature Enhancement for Person Re-identification

Abstract: Person re-identification (Re-ID) has important practical application value in intelligent video analysis. Due to the illumination, occlusion, and pose variation, person Re-ID is still a challenging problem. Some recent Re-ID methods based on ResNet-50 have achieved high accuracy, but performance degradation is caused by pose variation. To address this issue, Pose-Invariant Convolutional Baseline (PICB) embed with the proposed Pooling Fusion Block (PFB) is put forward as a new baseline for person Re-ID task. On the basis of PICB, an end-to-end network named Appearance-Enhanced Feature Learning Network (AEFLN) is proposed to simultaneously learn diversity body features and discriminative part features. Specially, a novel (DBFL) strategy is presented to learn diversity body features, which could alleviate the potential local minima problem generated by optimizing model with randomly initialized parameters in PFB. In addition, uniform part-level feature extractors are applied to learn part features, which compensates for body features’ lack of distinguishable local information. In testing phase, body features and part features are integrated to represent the enhanced appearance feature for each person image. Comprehensive experiments have demonstrated that our method can outperform the sate-of-the-art results on several public available datasets, including Market-1501, CUHK03 and DukeMTMC-reID. For instance, we achieve 74.8% (+11.1%) and 76.5% (+19.0%) in Rank-1 accuracy and mAP on CUHK03 dataset.

Real-Time Underwater Fish Tracking Based on Adaptive Multi-Appearance Model

Abstract: Tracking live fish in an open underwater environment to investigate their behavior is of great value for many applications, e.g. biological and robotic research. However, tracking fish in real world environment is a challenging task due to complex non-rigid deformation and abrupt movement of fish. In this paper, we explore and incorporate motion property of fish and propose a real-time fish tracking method based on novel adaptive multi-appearance models and tracking strategy, which can be adapted to various changes of the fish appearance caused by non-rigid deformation. Experimental results show the promising performance of the proposed method can outperform the previous method by 13.4% in accuracy on average and is robust to real-time underwater fish tracking.

Is Your First Impression Reliable? Trustworthy Analysis Using Facial Traits in Portraits

Abstract: As a basic human quality, trustworthiness plays an important role in social communications. In this paper, we proposed a novel concept to predict people’s trustworthiness at first sight using facial traits. Firstly, personality-toward traits were designed from psychology, including permanent traits and transient traits. Then, a mixture of feature descriptors consisting of Histogram of Gradients (HOG), Local Binary Patterns (LBP) and geometrical descriptions were adopted to describe personality traits. Finally, we trained the personality traits by LibSVM to determine trustworthiness of a person using portrait. Experiments demonstrated the effectiveness of our method by improving the precision by 33.60%, recall by 20.33% and F1-measure by 25.63% when determining whether a person is trustworthy or not comparing to a baseline method. Feature contribution analysis was applied to deeply unveil the correspondence between features and personality. Demonstration showed visual patterns in portrait collages of trustworthy people that further proved effectiveness of our method.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

A survey on wearable sensor modality centred human activity recognition in health care

Abstract: Increased life expectancy coupled with declining birth rates is leading to an aging population structure. Aging-caused changes, such as physical or cognitive decline, could affect people's quality of life, result in injuries, mental health or the lack of physical activity. Sensor-based human activity recognition (HAR) is one of the most promising assistive technologies to support older people's daily life, which has enabled enormous potential in human-centred applications. Recent surveys in HAR either only focus on the deep learning approaches or one specific sensor modality. This survey aims to provide a more comprehensive introduction for newcomers and researchers to HAR. We first introduce the state-of-art sensor modalities in HAR. We look more into the techniques involved in each step of wearable sensor modality centred HAR in terms of sensors, activities, data pre-processing, feature learning and classification, including both conventional approaches and deep learning methods. In the feature learning section, we focus on both hand-crafted features and automatically learned features using deep networks. We also present the ambient-sensor-based HAR, including camera-based systems, and the systems which combine the wearable and ambient sensors. Finally, we identify the corresponding challenges in HAR to pose research problems for further improvement in HAR.