Occlusion Detection for Long Term Visual Tracking

In this paper, we mainly address the problem of tracking a single ship in inland waterway CCTV (Closed-Circuit Television) video sequences. Although state-of-the-art performance has been demonstrated in TLD (Tracking-Learning-Detection) visual tracking, it is still challenging to perform long-term robust ship tracking due to factors such as cluttered background, scale change, partial or full occlusion and so forth. In this work, we focus on tracking a single ship when it suffers occlusion. To accomplish this goal, an effective Kalman filter is adopted to construct a novel online model to adapt to the rapid ship appearance change caused by occlusion. Experimental results on numerous inland waterway CCTV video sequences demonstrate that the proposed algorithm outperforms the original one.

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Astronaut Visual Tracking of Flying Assistant Robot in Space Station Based on Deep Learning and Probabilistic Model

International Journal of Aerospace Engineering ◽

10.1155/2018/6357185 ◽

2018 ◽

Vol 2018 ◽

pp. 1-17 ◽

Cited By ~ 5

Author(s):

Rui Zhang ◽

Zhaokui Wang ◽

Yulin Zhang

Keyword(s):

Feature Extraction ◽

Deep Learning ◽

Visual Tracking ◽

Probabilistic Model ◽

Color Image ◽

Space Station ◽

Depth Image ◽

Single Shot ◽

Occlusion Detection ◽

Robust Tracking

Real-time astronaut visual tracking is the most important prerequisite for flying assistant robot to follow and assist the served astronaut in the space station. In this paper, an astronaut visual tracking algorithm which is based on deep learning and probabilistic model is proposed. Fine-tuned with feature extraction layers’ parameters being initialized by ready-made model, an improved SSD (Single Shot Multibox Detector) network was proposed for robust astronaut detection in color image. Associating the detection results with synchronized depth image measured by RGB-D camera, a probabilistic model is presented to ensure accurate and consecutive tracking of the certain served astronaut. The algorithm runs 10 fps at Jetson TX2, and it was extensively validated by several datasets which contain most instances of astronaut activities. The experimental results indicate that our proposed algorithm achieves not only robust tracking of the specified person with diverse postures or dressings but also effective occlusion detection for avoiding mistaken tracking.

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Robust Visual Tracking Based on Adaptive Convolutional Features and Offline Siamese Tracker

Sensors ◽

10.3390/s18072359 ◽

2018 ◽

Vol 18 (7) ◽

pp. 2359 ◽

Cited By ~ 2

Author(s):

Ximing Zhang ◽

Mingang Wang

Keyword(s):

Visual Tracking ◽

Training Data ◽

Background Information ◽

Appearance Model ◽

Dimensionality Reduction Technique ◽

Wide Range ◽

Model Update ◽

Tracking Ability ◽

Novel Method

Robust and accurate visual tracking is one of the most challenging computer vision problems. Due to the inherent lack of training data, a robust approach for constructing a target appearance model is crucial. The existing spatially regularized discriminative correlation filter (SRDCF) method learns partial-target information or background information when experiencing rotation, out of view, and heavy occlusion. In order to reduce the computational complexity by creating a novel method to enhance tracking ability, we first introduce an adaptive dimensionality reduction technique to extract the features from the image, based on pre-trained VGG-Net. We then propose an adaptive model update to assign weights during an update procedure depending on the peak-to-sidelobe ratio. Finally, we combine the online SRDCF-based tracker with the offline Siamese tracker to accomplish long term tracking. Experimental results demonstrate that the proposed tracker has satisfactory performance in a wide range of challenging tracking scenarios.

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