Enhancement of ELDA Tracker Based on CNN Features and Adaptive Model Update

Appearance representation and the observation model are the most important components in designing a robust visual tracking algorithm for video-based sensors. Additionally, the exemplar-based linear discriminant analysis (ELDA) model has shown good performance in object tracking. Based on that, we improve the ELDA tracking algorithm by deep convolutional neural network (CNN) features and adaptive model update. Deep CNN features have been successfully used in various computer vision tasks. Extracting CNN features on all of the candidate windows is time consuming. To address this problem, a two-step CNN feature extraction method is proposed by separately computing convolutional layers and fully-connected layers. Due to the strong discriminative ability of CNN features and the exemplar-based model, we update both object and background models to improve their adaptivity and to deal with the tradeoff between discriminative ability and adaptivity. An object updating method is proposed to select the “good” models (detectors), which are quite discriminative and uncorrelated to other selected models. Meanwhile, we build the background model as a Gaussian mixture model (GMM) to adapt to complex scenes, which is initialized offline and updated online. The proposed tracker is evaluated on a benchmark dataset of 50 video sequences with various challenges. It achieves the best overall performance among the compared state-of-the-art trackers, which demonstrates the effectiveness and robustness of our tracking algorithm

Exemplar-based Linear Discriminant Analysis for Robust Object Tracking

Tracking-by-detection has become an attractive tracking technique, which treats tracking as a category detection problem. However, the task in tracking is to search for a specific object, rather than an object category as in detection. In this paper, we propose a novel tracking framework based on exemplar detector rather than category detector. The proposed tracker is an ensemble of exemplar-based linear discriminant analysis (ELDA) detectors. Each detector is quite specific and discriminative, because it is trained by a single object instance and massive negatives. To improve its adaptivity, we update both object and background models. Experimental results on several challenging video sequences demonstrate the effectiveness and robustness of our tracking algorithm.Comment: ICIP201

Enhancement of ELDA Tracker Based on CNN Features and Adaptive Model Update

Appearance representation and the observation model are the most important components in designing a robust visual tracking algorithm for video-based sensors. Additionally, the exemplar-based linear discriminant analysis (ELDA) model has shown good performance in object tracking. Based on that, we improve the ELDA tracking algorithm by deep convolutional neural network (CNN) features and adaptive model update. Deep CNN features have been successfully used in various computer vision tasks. Extracting CNN features on all of the candidate windows is time consuming. To address this problem, a two-step CNN feature extraction method is proposed by separately computing convolutional layers and fully-connected layers. Due to the strong discriminative ability of CNN features and the exemplar-based model, we update both object and background models to improve their adaptivity and to deal with the tradeoff between discriminative ability and adaptivity. An object updating method is proposed to select the “good” models (detectors), which are quite discriminative and uncorrelated to other selected models. Meanwhile, we build the background model as a Gaussian mixture model (GMM) to adapt to complex scenes, which is initialized offline and updated online. The proposed tracker is evaluated on a benchmark dataset of 50 video sequences with various challenges. It achieves the best overall performance among the compared state-of-the-art trackers, which demonstrates the effectiveness and robustness of our tracking algorithm

Prediction of Physical Properties of Crude Oil Based on Ensemble Random Weights Neural Network

Lu J, Ding J, Liu C, Jin Y. Prediction of Physical Properties of Crude Oil Based on Ensemble Random Weights Neural Network. IFAC-PapersOnLine. 2018;51(18):655-660.Prediction of physical properties of crude oil plays a key role in the petroleum refining industry, therefore, it is of great significance to establish the prediction model of physical properties of crude oil. In this paper, we propose an ensemble random weights neural network based prediction model whose inputs are nuclear magnetic resonance (NMR) spectra and outputs are carbon residual and asphaltene of crude oil. The model uses random vector functional link (RVFL) networks as the basic components and employs the regularized negative correlation learning strategy to build neural network ensemble and the online method to learn the new data. The experiment using the practical data collected from a refinery is carried out and compared with the decorrelated neural network ensembles with random weights (DNNE), least squares support vector machine (LS-SVM), partial least squares regression (PLS) and multiple linear regression (MLR). The results indicate the effectiveness of the proposed approach

Enhancement of ELDA Tracker Based on CNN Features and Adaptive Model Update

Appearance representation and the observation model are the most important components in designing a robust visual tracking algorithm for video-based sensors. Additionally, the exemplar-based linear discriminant analysis (ELDA) model has shown good performance in object tracking. Based on that, we improve the ELDA tracking algorithm by deep convolutional neural network (CNN) features and adaptive model update. Deep CNN features have been successfully used in various computer vision tasks. Extracting CNN features on all of the candidate windows is time consuming. To address this problem, a two-step CNN feature extraction method is proposed by separately computing convolutional layers and fully-connected layers. Due to the strong discriminative ability of CNN features and the exemplar-based model, we update both object and background models to improve their adaptivity and to deal with the tradeoff between discriminative ability and adaptivity. An object updating method is proposed to select the “good” models (detectors), which are quite discriminative and uncorrelated to other selected models. Meanwhile, we build the background model as a Gaussian mixture model (GMM) to adapt to complex scenes, which is initialized offline and updated online. The proposed tracker is evaluated on a benchmark dataset of 50 video sequences with various challenges. It achieves the best overall performance among the compared state-of-the-art trackers, which demonstrates the effectiveness and robustness of our tracking algorithm

Impact of Government Policies on Research and Development (R&D) Investment, Innovation, and Productivity: Evidence from Pesticide Firms in China

China’s pesticide industry has played an important role in the growth of agricultural productivity in both China and worldwide, but its development is hampered by production inefficiency and the lack of innovation of active ingredients. To improve innovation and the productivity of the pesticide industry, the Chinese government has implemented a series of policies to stimulate private research and development (R&D) and firm innovation. Using the firm-level panel data of the Annual Survey of Industrial Firms (ASIF) collected by the National Bureau of Statistics in 2001–2007, this study examines the linkages between R&D investment, innovation, and productivity with a focus on the role of government policies. The results show that pesticide firms with a higher intensity of R&D investment were associated with a higher patent intensity, and more innovated firms were associated with a higher productivity. Public research, intellectual property enforcement, production subsidy, foreign direct investment (FDI), and being export oriented were positively associated with the innovation and productivity of pesticide firms