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Object tracking is one of the most important problems in computer vision applications such as robotics, autonomous driving, and pedestrian movement. There has been a significant development in camera hardware where researchers are experimenting with the fusion of different sensors and developing image processing algorithms to track objects. Image processing and deep learning methods have significantly progressed in the last few decades. Different data association methods accompanied by image processing and deep learning are becoming crucial in object tracking tasks. The data requirement for deep learning methods has led to different public datasets that allow researchers to benchmark their methods. While there has been an improvement in object tracking methods, technology, and the availability of annotated object tracking datasets, there is still scope for improvement. This review contributes by systemically identifying different sensor equipment, datasets, methods, and applications, providing a taxonomy about the literature and the strengths and limitations of different approaches, thereby providing guidelines for selecting equipment, methods, and applications. Research questions and future scope to address the unresolved issues in the object tracking field are also presented with research direction guidelines. [ABSTRACT FROM AUTHOR]

A deblocking method based on projection onto convex sets (POCS) is proposed to reduce blocking artifacts in compressed images coded by the block discrete cosine transform. The method differs from existing POCS-based methods in three aspects. Firstly, the adjustment of a pixel's intensity is determined by local properties of the pixel. Secondly, three locally adaptive constraint sets are introduced to improve deblocking results. Thirdly, the human visual system modeling and relaxed projections are incorporated to make pixels adjust appropriately. The method is tested on typical images with excellent results. Index Terms--Block discrete cosine transform (BDCT}, blocking artifact, JPEG, MPEG, projection onto convex sets (POCS).

The presence of shadows in images significantly affects the performance of many computer vision tasks and visual processing applications, such as object tracking, object classification, and behavior recognition. Most methods have been designed to detect shadows in specific situations, but they often fail to distinguish shadow points from the foreground object in many problematic situations, such as chromatic shadows, non-textured and dark surfaces, and foreground–background camouflage. In this paper, we propose a new feature-based image patch approximation and multi-independent sparse representation technique to tackle these environmental problems. In this method, two illumination-invariant features—binary patterns of local color constancy and light-based gradient matching—are introduced, along with the intensity-reduction histogram. These features are extracted from image patches and are used to construct two over-complete dictionaries for objects and shadows, respectively. Given a new image patch, its best approximation for a number of iterations is found from each dictionary. For each iteration, an independent class assignment is performed by finding its distances from the reference dictionaries. The patch is then assigned to a class based on its probability of occurrence. The proposed framework is evaluated on common shadow detection data sets, and it shows improved performance in terms of the shadow detection rate and discrimination rate compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Colour detection plays an important role for many computer vision-based applications. However, most existing colour detection methods tend to be environment dependent since slight changes of environmental factors such as illumination or shadowing effects could greatly reduce their performances. In this paper, a new colour model is introduced to allow enhanced colour detection from images, even with significantly different lighting conditions and image qualities. The proposed colour model is called the HPBr colour model. It is converted from the RGB colour model and it consists of three colour components, namely, hue (H), purity (P) and brightness (Br). This colour model can be represented in three different geometric shapes: diamond, sphere and cylinder. To assess the effectiveness of the model, two different colour detection methods have been applied onto benchmark images. Experimental results from both methods confirmed that the proposed colour model produced the best colour detection results among existing models. [ABSTRACT FROM AUTHOR]

A method of early smoke detection in video using swaying and diffusion feature is presented in this paper. Firstly, in view of early smoke's swaying feature, choquet fuzzy integral was adopted to extract dynamic regions from video frames, and then, a swaying identification algorithm based on centroid calculation was used to distinguish candidate smoke region from other dynamic regions. Secondly, smoke diffusion makes different textures between the bottom region and the top region of smoke. This unique feature was used to differentiate smoke from other candidate smoke regions by Gray Level Co-occurrence Matrix. Experiments show that the proposed method is effective, robust, and has a performance of earlier smoke alarm. The processing rate of the smoke detection method achieves 25 frames per second with an image size of 320 × 240 pixels. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an algorithm with the purpose of improving upon the already successful constrained Delaunay triangulation (CDT) skeletonisation technique. Using such a triangulation to construct a skeleton has proven very effective, that can sometimes, however, produce triangles that do not represent the true nature of the underlying shape. The contour pixels chosen for triangulation are of significant importance, as they determine the triangle edges that define the skeleton. The algorithm described in this paper deals with this problem by inserting new triangulation points in strategic locations in end, normal and junction triangles. Results show that the skeletons produced by this algorithm are accurate, robust against noise and, above all, comply much better with a human''s perception of the image than the original triangulation method. [Copyright &y& Elsevier]

Abstract: The accuracy of a non-pixel-based skeletonization method is largely dependent on the contour information chosen as input. When using a Constrained Delaunay Triangulation to construct an object''s skeleton, a number of contour pixels must be chosen as a basis for triangulation. This paper presents a new method of selecting these contour pixels. A new method for measuring skeletonization error is proposed, which quantifies the deviation of a skeleton segment from the true medial axis of a stroke in an image. The goal of the proposed algorithm is to reduce this error to an acceptable level, whilst retaining the superior efficiencies of previous non-pixel-based techniques. Experimental results show that the proposed method is adept at following the medial axis of an image, and is capable of producing a skeleton that is confirmed by a human''s perception of the image. It is also computationally efficient and robust against noise. [Copyright &y& Elsevier]