Identity Retention of Multiple Objects under Extreme Occlusion Scenarios using Feature Descriptors

Identity assignment and retention needs multiple object detection and tracking. It plays a vital role in behavior analysis and gait recognition. The objective of Multiple Object Tracking (MOT) is to detect, track and retain identities from an image sequence. An occlusion is a major resistance in identity retention. It is a challenging task to handle occlusion while tracking varying number of person in the complex scene using a monocular camera. In MOT, occlusion remains a challenging task in real world applications. This paper uses Gaussian Mixture Model (GMM) and Hungarian Assignment (HA) for person detection and tracking. We propose an identity retention algorithm using Rotation Scale and Translation (RST) invariant feature descriptors. In addition, a segmentation based optimum demerge handling algorithm is proposed to retain proper identities under occlusion. The proposed approach is evaluated on a standard surveillance dataset sequences and it achieves 97 % object detection accuracy and 85% tracking accuracy for PETS-S2.L1 sequence and 69.7% accuracy as well as 72.3% precision for Town Centre Sequence

An Efficient Image Fusion of Visible and Infrared Band Images using Integration of Anisotropic Diffusion and Discrete Wavelet Transform

Image fusion is a technique that combines two source images to generate more informative target image. It plays a vital role in medical image investigation, military, navigation, etc. visible images offer efficient texture detail with high spatial resolution. In contrast, based on the radiation difference infrared images are able to differentiate target from their background. There are many algorithms that helps in preserving the edges of image like Bilateral filter, anisotropic diffusion (ADF). This paper integrates Anisotropic Diffusion and Karhunen-Loeve (KL)Transformation with discrete wavelet transform (DWT). In proposed Method, DWT decomposes into four sub-bands. ADF is applied on approximation sub-band and absolute maximum selection is applied on other three sub-bands. ADF decomposes the image into detailed layer and base layer. Base layer and Detailed layer are calculated using Kl- Transformation and linear combination respectively. Once fusion is done, inverse DWT is applied on all sub-bands. The experimental outcomes depict that the offered approach result with sharp edges of the image. The proposed algorithm is evaluated on standard dataset Like Duine_Sequence, Tree_sequence, Street dataset. Standard metrics like Average Gradients and Spatial Frequency metrics are used to evaluate the performance of the image

Glacier mapping with object based image analysis method, case study of Mount Everest region

Substantial progress in Geoinformatics System in recent years leads to the research in monitoring and mapping of glaciers. Monitoring glacier in the mountain region with traditional manual method is very crucial and time-consuming. Glaciers are melting because of global warming. Melting of glaciers can causes calamities like rising in sea level, glacial lake outburst, avalanches etc. Glacier monitoring using multi-temporal data for objects on the surface of the glacier is hard to classify. This paper gives an insight into the importance of Geo-spatial data and object-based image analysis method for satellite image processing. The object-based image analysis benefits more compared to traditional pixel-based image analysis as it is more robust and noise removing more image features etc. Spectral data with multiple bands is the backbone of surveying and monitoring of glacier. In this paper case study of Mount Everest region (27 48° 32N, 86 54° 47E) is represented. The remotely sensed image needs to be taken in a cloud-free environment. Object-based image classification is done in recognition tool. Also, the step by step methodology of object-based classification, segmentation and post-classification possibilities are discussed. Finally, the paper presents several representations of indexes. The integration of indexes is useful for accurately classifying the different part of terrain, lake, vegetation and glacier

Path planning optimization and object placement through visual servoing technique for robotics application

Visual servoing define a new methodology for vision based control in robotics. Vision based action involve number of actions that move a robot in response of results of camera analysis. This process is important to operate and help robot to achieve a specific goal. The main purpose of visual servoing consists of considering a vision system by specific sensor dedicated to involve control servo loop and task. In this article, three visual control scheme: Image Based Visual Servoing (IBVS), Position Based Visual Servoing (PBVS) and Hybrid Based Visual Servoing (HBVS) are illustrated. The different terminologies are represented through effective workflow of robot vision. IBVS method concentrate on the image features that are immediately available in the image. This experiment is performed by estimating distance between camera and object. PBVS consist of moving object 3-D parameters to estimate measurement. This paper showcases PBVS using kuka robot model. HBVS uses the 2D and 3D servoing by combining visual sensors also it overcomes challenges of previous two methods. This paper represents HBVS method using IPR communication robot model