IIIT Hyderabad Publications |
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Object Search Using Context InformationAuthor: Sheetal Reddy Date: 2019-11-28 Report no: IIIT/TH/2019/122 Advisor:Madhava Krishna AbstractThe advent of indoor personal mobile robots has clearly demonstrated their utility in assisting humans at various places such as workshops, offices, homes, etc. One of the most important cases in such autonomous scenarios is where the robot has to search for certain objects in large rooms. Exploring the whole room would prove to be extremely expensive in terms of both computing power and time. To address this issue, we demonstrate a fast algorithm to reduce the search space by identifying possible object locations as two classes, namely - Support Structures and Clutter. Support Structures are plausible object containers in a scene, such as tables, chairs, sofas, etc. Clutter refers to places where there seem to be several objects but cannot be clearly distinguished. In this thesis, we propose an algorithm to identify potential object locations using a Support Vector Machine(SVM) learned over the features extracted from the depth map and the RGB image of the scene, which further culminates into a densely connected Conditional Random Field(CRF) formulated over the image of the scene. The inference over the CRF leads to the assignment of the labels - support structure, clutter, others to each pixel. Further to this, we also explore deep neural network-based algorithms for detecting objects from far off spaces. Small object detection plays an important role in robot navigation, scene understanding, etc as most of the objects observed are generally small until observed from close. Although current object detection algorithms show state-of-the-art accuracy on multiple datasets, they fail to accurately detect small objects. We propose a deep neural network architecture which is a variant of the SSD network. The proposed architecture uses spatial context to efficiently detect small objects without much reduction in the speed. This is close to mimicking human behavior where we search for small objects in the vicinity of larger related objects. The architecture proposed shows a better accuracy for small object detection compared to the conventional SSD. We also explore the use of a context network-based method to improve the accuracy of small object detection. We formulate this as a label space learning method compared to an end-to-end learning algorithm. We use the state-of-the-art YOLO network and train a context network as an auxiliary task for better localization of the small objects in an image. We demonstrate improvements in the object detection accuracy for small objects compared to the YOLO v2 method. Full thesis: pdf Centre for Robotics |
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