Visual tracking is a fundamental research problem in computer vision field. In this paper, we propose an approach to incorporate visual prior into visual object tracking via deep neural network. Visual prior knowledge...
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ISBN:
(纸本)9781467365529
Visual tracking is a fundamental research problem in computer vision field. In this paper, we propose an approach to incorporate visual prior into visual object tracking via deep neural network. Visual prior knowledge is expressed as the parameters of a stacked denoising autoencoder, which is trained from a large collection of natural images. By utilizing natural images, we can obtain generic image features which are more robust against variations. Then we design a classifier for tracking using the same structure as the stacked denoising autoencoder, tracking is then carried out under a particle filter framework by determining the current target's location and updating the parameters. In addition, in order to alleviate the computational burden caused by deep structure, an adaptive updating mechanism is proposed. As a result, we apply a general-to-special strategy for our stacked denoising autoencoder tracker (SDAT), the learned visual prior provides a reasonable initial value for parameters of the neural network, and the deep structure of our tracker is robust to appearance variations. Experiments over 50 challenging videos indicate the effectiveness and robustness of our tracker, and the resulting tracker is outstanding especially against variations with the existing state-of-the-art methods.
In service engineering it is important to estimate when and what a worker did, because they include crucial evidences to improve service quality and working environments. For Service Operation Estimation (SOE), acoust...
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ISBN:
(纸本)9780992862633
In service engineering it is important to estimate when and what a worker did, because they include crucial evidences to improve service quality and working environments. For Service Operation Estimation (SOE), acoustic information is one of useful and key modalities;particularly environmental or background sounds include effective cues. This paper focuses on two aspects: (1) extracting powerful and robust acoustic features by using stacked-denoising-autoencoder and hag-of-feature techniques, and (2) investigating a multi-modal SOE scheme by combining the audio features and the other sensor data as well as non-sensor information. We conducted evaluation experiments using multi-modal data recorded in a restaurant. We improved SOE performance in comparison to conventional acoustic features, and effectiveness of our multi modal SOE scheme is also clarified.
Trajectories obtained from low level tracking algorithm provide an opportunity for us to analyze meaningful behaviors and monitor adverse or malicious events. How to abstract meaningful features from the raw data of t...
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ISBN:
(纸本)9780982443866
Trajectories obtained from low level tracking algorithm provide an opportunity for us to analyze meaningful behaviors and monitor adverse or malicious events. How to abstract meaningful features from the raw data of trajectories is a challenge due to the high dimensionality and noise. In this paper, a novel approach, stacked denoising autoencoder(SDA) is applied to address this problem. This method can reduce the dimensionality of the trajectories significantly, so that they can be handled easily. More importantly, the denoising process of the SDA can capture the structure of the raw data, so the features they producing generalize well for detecting anomalous trajectories. The results of the numerical experiments prove the validity of the proposed approach.
In service engineering it is important to estimate when and what a worker did, because they include crucial evidences to improve service quality and working environments. For Service Operation Estimation (SOE), acoust...
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ISBN:
(纸本)9781479988518
In service engineering it is important to estimate when and what a worker did, because they include crucial evidences to improve service quality and working environments. For Service Operation Estimation (SOE), acoustic information is one of useful and key modalities;particularly environmental or background sounds include effective cues. This paper focuses on two aspects: (1) extracting powerful and robust acoustic features by using stacked-denoising-autoencoder and bag-of-feature techniques, and (2) investigating a multi-modal SOE scheme by combining the audio features and the other sensor data as well as non-sensor information. We conducted evaluation experiments using multi-modal data recorded in a restaurant. We improved SOE performance in comparison to conventional acoustic features, and effectiveness of our multi-modal SOE scheme is also clarified.
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