In this article, a particle filter based tracking algorithm is proposed to track a target in video with vivid and complex environments. The target is represented in feature space by both color distribution and KAZE features. Color distribution is selected for its robustness to target's scale variation and partial occlusion. KAZE features are chosen for their ability to represent the target structure and also for their superior performance in feature matching. Fusion of these two features will lead to effective tracking as compared to other features due to their better representational abilities, under challenging conditions. The trajectory of the target is established using the particle filter algorithm based on similarity between the extracted features from the target and the probable candidates in the consecutive frames. For the color distribution model, Bhattacharya coefficient is used as a similarity metric whereas Nearest Neighbor Distance Ratio is used for matching of corresponding feature points in KAZE algorithm. The particle filter update model is based on kinematic motion equations and the weights on particles are governed by an equation fusing both the color and KAZE features. Centre Location Error, Average Tracking Accuracy and Tracking Success Rate are the performance metrics considered in the evaluation process. Also, the overlap success plot and precision plot is considered for performance evaluation. On the basis of these metrics and visual results obtained under different environment conditions: outdoor, occluding and underwater ones, the proposed tracking scheme performs significantly better than the contemporary feature-based iterative object tracking methods and even few of the learning-based algorithms.

Background subtraction is an essential step in many computer vision tasks. In this article, we put forth a unique attempt to detect the local changes in challenging video scenes by exploring the capabilities of an encoder-decoder type network that employs a modified ResNet-152 architecture with a multi-scale features extraction framework. The proposed encoder network consists of a modified ResNet-152 network where the initial two blocks are freeze and the weights of the last blocks are learned using a transfer learning mechanism. The said encoder can reduce the computational complexity of the proposed model and extract fine as well as coarse-scale features. We have proposed a multiscale features extraction (MFE) mechanism block which is a hybridization of pyramidal pooling architecture (PPA), and various atrous convolutional layers where the high-level features from the encoder network are utilized to extract features at various scales. The use of PPA in the MFE block preserves maximum value in every pooling area, to retain the contextual relationship between the pixels in the complex video frames that can handle various challenging scenes. The proposed decoder network consists of stacked transposed convolution layers that learn a mapping from feature space to image space, predicting a score map. Then, a threshold is applied on the score map to get the binary class labels as the background and foreground. The shortcut connections followed by global average pooling (GAP) drive the low-level feature coefficients from the encoder network to the decoder network to enhance the feature representation. The performance of the proposed scheme is validated by testing it against thirty-one state-of-the-art techniques. The results obtained by the proposed method are corroborated qualitatively as well as quantitatively. Further, the efficacy of the proposed algorithm is verified with an unseen video setup and is found to provide better performance.

In this paper, we present a novel algorithm for moving object detection and tracking. The proposed algorithm includes two schemes: one for spatio-temporal spatial segmentation and the other for temporal segmentation. A combination of these schemes is used to identify moving objects and to track them. A compound Markov random field (MRF) model is used as the prior image attribute model, which takes care of the spatial distribution of color, temporal color coherence and edge map in the temporal frames to obtain a spatio-temporal spatial segmentation. In this scheme, segmentation is considered as a pixel labeling problem and is solved using the maximum a posteriori probability (MAP) estimation technique. The MRF-MAP framework is computation intensive due to random initialization. To reduce this burden, we propose a change information based heuristic initialization technique. The scheme requires an initially segmented frame. For initial frame segmentation, compound MRF model is used to model attributes and MAP estimate is obtained by a hybrid algorithm [combination of both simulated annealing (SA) and iterative conditional mode (ICM)] that converges fast. For temporal segmentation, instead of using a gray level difference based change detection mask (CDM), we propose a CDM based on label difference of two frames. The proposed scheme resulted in less effect of silhouette. Further, a combination of both spatial and temporal segmentation process is used to detect the moving objects. Results of the proposed spatial segmentation approach are compared with those of JSEG method, and edgeless and edgebased approaches of segmentation. It is noticed that the proposed approach provides a better spatial segmentation compared to the other three methods.

Quality assessment of 3D-synthesized images has traditionally been based on detecting specific categories of distortions such as stretching, black-holes, blurring, etc. However, such approaches have limitations in accurately detecting distortions entirely in 3D synthesized images affecting their performance. This work proposes an algorithm to efficiently detect the distortions and subsequently evaluate the perceptual quality of 3D synthesized images. The process of generation of 3D synthesized images produces a few pixel shift between reference and 3D synthesized image, and hence they are not properly aligned with each other. To address this, we propose using morphological operation (opening) in the residual image to reduce perceptually unimportant information between the reference and the distorted 3D synthesized image. The residual image suppresses the perceptually unimportant information and highlights the geometric distortions which significantly affect the overall quality of 3D synthesized images. We utilized the information present in the residual image to quantify the perceptual quality measure and named this algorithm as Perceptually Unimportant Information Reduction (PU-IR) algorithm. At the same time, the residual image cannot capture the minor structural and geometric distortions due to the usage of erosion operation. To address this, we extract the perceptually important deep features from the pre-trained VGG-16 architectures on the Laplacian pyramid. The distortions in 3D synthesized images are present in patches, and the human visual system perceives even the small levels of these distortions. With this view, to compare these deep features between reference and distorted image, we propose using cosine similarity and named this algorithm as Deep Features extraction and comparison using Cosine Similarity (DF-CS) algorithm. The cosine similarity is based upon their similarity rather than computing the magnitude of the difference of deep features. Finally, the pooling is done to obtain the objective quality scores using simple multiplication to both PU-IR and DF-CS algorithms. Our source code is available online: https://github.com/sadbhawnathakur/3D-Image-Quality-Assessment .