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Title General Object Detection Using Superpixel Preprocessing
Publication Date
Discipline/Department Computer Vision
University/Publisher Linköping University
Abstract The objective of this master’s thesis work is to evaluate the potential benefit of a superpixel preprocessing step for general object detection in a traffic environment. The various effects of different superpixel parameters on object detection performance, as well as the benefit of including depth information when generating the superpixels are investigated. In this work, three superpixel algorithms are implemented and compared, including a proposal for an improved version of the popular Spectral Linear Iterative Clustering superpixel algorithm (SLIC). The proposed improved algorithm utilises a coarse-to-fine approach which outperforms the original SLIC for high-resolution images. An object detection algorithm is also implemented and evaluated. The algorithm makes use of depth information obtained by a stereo camera to extract superpixels corresponding to foreground objects in the image. Hierarchical clustering is then applied, with the segments formed by the clustered superpixels indicating potential objects in the input image. The object detection algorithm managed to detect on average 58% of the objects present in the chosen dataset. It performed especially well for detecting pedestrians or other objects close to the car. Altering the density distribution of the superpixels in the image yielded an increase in detection rate, and could be achieved both with or without utilising depth information. It was also shown that the use of superpixels greatly reduces the amount of computations needed for the algorithm, indicating that a real-time implementation is feasible.
Subjects/Keywords superpixels; SLIC; coarse-to-fine; segmentation; general object detection; cityscapes; traffic; image processing; clustering; Computer Vision and Robotics (Autonomous Systems); Datorseende och robotik (autonoma system)
Language en
Country of Publication se
Record ID oai:DiVA.org:liu-140874
Repository diva
Date Indexed 2020-01-03

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…40 40 43 45 45 47 52 5 Discussion 5.1 Results for Superpixel Segmentation . . . . . . . . 5.1.1 SLIC . . . . . . . . . . . . . . . . . . . . . . 5.1.2 CTF . . . . . . . . . . . . . . . . . . . . . . . 5.1.3 CTF-SLIC

…superpixel (right). 2.2 Related Work One of the most popular superpixel algorithms is Spectral Linear Iterative Clustering (SLIC), proposed by Achanta et al. in 2010 [1]. The algorithm performs a version of k-means clustering1…

…to the relative simplicity of the algorithm, many methods of improving its performance have been suggested. Examples include algorithms such as S-SLIC [9] and gSLIC [17], which successfully enable the computation of SLIC

…superpixels in real-time. gSLIC in particular shows interesting results, as it allows for a speedup of the basic SLIC algorithm by up to 20 times when implementing hardware acceleration. Another method for segmenting superpixels is given by Van den Bergh et al…

…In this thesis, the potential use of a coarse-to-fine update strategy confined to superpixel boundaries for use together with the straightforward SLIC algorithm is investigated. The algorithm is compared to an implementation of the basic SLIC

…Clustering (SLIC) - A superpixel algorithm proposed by Achanta et.al. in [2]. It is popular due to its simplicity and performance. 2. Coarse-To-Fine Superpixel Segmentation (CTF) - A modified version the coarse-to-fine block…

…update algorithms presented in [22, 24]. 3. Coarse-To-Fine Spectral Linear Iterative Clustering (CTF-SLIC) - An algorithm which is proposed in this thesis as an improvement of SLIC, combining it with the coarse-to-fine update strategy…

…from [22, 24]. This section presents each of the three algorithms and provides general descriptions of how they work. 2.3.1 Spectral Linear Iterative Clustering (SLIC) In this work, the regular version of SLIC as presented in…