Viewpoint Improvements for Object Detection with Multitask Learning


Viewpoint Improvements for Object Detection with Multitask Learning – Understanding and improving the performance of intelligent vehicles is a challenging task due to the many challenges in the autonomous driving scene. Recent findings in computer vision show that the detection of movement poses of the vehicles is often affected by multiple factors such as vehicle interaction and object rotation, pose, location, and visibility. While the performance of autonomous vehicles is improving in recent years, it is still an open challenge to tackle these challenges. In this work, we propose an online CNN-based approach for vehicle navigation through traffic in congested roadways to improve recognition performance. The proposed approach is based on a novel, deep learning-based method to extract features extracted from the images of the roadways. We first train a deep convolutional network (DCNN) trained on high-resolution roadimages. Then, an online ConvNet is learned to learn a distance metric to predict a vehicle’s pose, pose, and visibility based on the extracted features. Finally, the proposed CNN is used for segmentation of the vehicle. At test time, the vehicle is shown to be able to navigate through roads without the need of human assistance or human presence.

The success of recent deep learning-based vision systems for object localization has led to the development of large-scale object localization systems. These systems are challenging in that the tasks are hard for humans to do and humans usually cannot track objects at all and most of objects have no geometric appearance (such as their position). Thus, this work proposes a novel deep learning-based learning system to classify objects at multiple levels of the scene. This system aims at solving multi-dimensional object localization tasks such as object detection, object appearance, and object pose, using object detection and pose matching as two crucial components. The proposed system was trained using 3D-LSTM and trained using a convolutional neural network (CNN), aiming at identifying objects on the first level and the object pose over multiple levels. The system evaluated its effectiveness on object detection task including detection of the objects at the second, third and fourth levels (from the first to the second). Results show that our algorithm significantly improved the overall performance on the problem of object detection and pose matching.

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Viewpoint Improvements for Object Detection with Multitask Learning

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  • A Note on the SPICE Method and Stability Testing

    Learning Unsupervised Object Localization for 6-DoF Scene LabelingThe success of recent deep learning-based vision systems for object localization has led to the development of large-scale object localization systems. These systems are challenging in that the tasks are hard for humans to do and humans usually cannot track objects at all and most of objects have no geometric appearance (such as their position). Thus, this work proposes a novel deep learning-based learning system to classify objects at multiple levels of the scene. This system aims at solving multi-dimensional object localization tasks such as object detection, object appearance, and object pose, using object detection and pose matching as two crucial components. The proposed system was trained using 3D-LSTM and trained using a convolutional neural network (CNN), aiming at identifying objects on the first level and the object pose over multiple levels. The system evaluated its effectiveness on object detection task including detection of the objects at the second, third and fourth levels (from the first to the second). Results show that our algorithm significantly improved the overall performance on the problem of object detection and pose matching.


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