Semi-Supervised Learning for Object Detection

University essay from Linköpings universitet/ReglerteknikLinköpings universitet/Tekniska högskolan


Many automotive safety applications in modern cars make use of cameras and object detection to analyze the surrounding environment. Pedestrians, animals and other vehicles can be detected and safety actions can be taken before dangerous situations arise.

To detect occurrences of the different objects, these systems are traditionally trained to learn a classification model using a set of images that carry labels corresponding to their content. To obtain high performance with a variety of object appearances, the required amount of data is very large. Acquiring unlabeled images is easy, while the manual work of labeling is both time-consuming and costly. Semi-supervised learning refers to methods that utilize both labeled and unlabeled data, a situation that is highly desirable if it can lead to improved accuracy and at the same time alleviate the demand of labeled data. This has been an active area of research in the last few decades, but few studies have investigated the performance of these algorithms in larger systems.

In this thesis, we investigate if and how semi-supervised learning can be used in a large-scale pedestrian detection system. With the area of application being automotive safety, where real-time performance is of high importance, the work is focused around boosting classifiers. Results are presented on a few publicly available UCI data sets and on a large data set for pedestrian detection captured in real-life traffic situations. By evaluating the algorithms on the pedestrian data set, we add the complexity of data set size, a large variety of object appearances and high input dimension.

It is possible to find situations in low dimensions where an additional set of unlabeled data can be used successfully to improve a classification model, but the results show that it is hard to efficiently utilize semi-supervised learning in large-scale object detection systems. The results are hard to scale to large data sets of higher dimensions as pair-wise computations are of high complexity and proper similarity measures are hard to find.

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