Object Detection using Particle Swarm Optimisation and Kalman Filter to Track Partially occluded Targets

Motion estimation, object detection, and tracking have been actively pursued by researchers in the field of real time video processing. In the present work, a new algorithm is proposed to automatically detect objects using revised local binary pattern (m-LBP) for object detection. The detected object was tracked and its location estimated using the Kalman filter, whose state covariance matrix was tuned using particle swarm optimisation (PSO). PSO, being a nature inspired algorithm, is a well proven optimization technique. This algorithm was applied to important real-world problems of partially-occluded objects in infrared videos. Algorithm validation was performed by realizing a thermal imager, and this novel algorithm was implemented in it to demonstrate that the proposed algorithm is more efficient and produces better results in motion estimation for partially-occluded objects. It is also shown that track convergence is 56% faster in the PSO-Kalman algorithm than tracking with Kalman-only filter.

Deep Reinforcement Learning-Based Robotic Grasping in Clutter and Occlusion

In robotic manipulation, object grasping is a basic yet challenging task. Dexterous grasping necessitates intelligent visual observation of the target objects by emphasizing the importance of spatial equivariance to learn the grasping policy. In this paper, two significant challenges associated with robotic grasping in both clutter and occlusion scenarios are addressed. The first challenge is the coordination of push and grasp actions, in which the robot may occasionally fail to disrupt the arrangement of the objects in a well-ordered object scenario. On the other hand, when employed in a randomly cluttered object scenario, the pushing behavior may be less efficient, as many objects are more likely to be pushed out of the workspace. The second challenge is the avoidance of occlusion that occurs when the camera itself is entirely or partially occluded during a grasping action. This paper proposes a multi-view change observation-based approach (MV-COBA) to overcome these two problems. The proposed approach is divided into two parts: 1) using multiple cameras to set up multiple views to address the occlusion issue; and 2) using visual change observation on the basis of the pixel depth difference to address the challenge of coordinating push and grasp actions. According to experimental simulation findings, the proposed approach achieved an average grasp success rate of 83.6%, 86.3%, and 97.8% in the cluttered, well-ordered object, and occlusion scenarios, respectively.

A Robust Face Recognition Algorithm Based on an Improved Generative Confrontation Network

Objective: In practical applications, an image of a face is often partially occluded, which decreases the recognition rate and the robustness. Therefore, in response to this situation, an effective face recognition model based on an improved generative adversarial network (GAN) is proposed. Methods: First, we use a generator composed of an autoencoder and the adversarial learning of two discriminators (local discriminator and global discriminator) to fill and repair an occluded face image. On this basis, the Resnet-50 network is used to perform image restoration on the face. In our recognition framework, we introduce a classification loss function that can quantify the distance between classes. The image generated by the generator can only capture the rough shape of the missing facial components or generate the wrong pixels. To obtain a clearer and more realistic image, this paper uses two discriminators (local discriminator and global discriminator, as mentioned above). The images generated by the proposed method are coherent and minimally influence facial expression recognition. Through experiments, facial images with different occlusion conditions are compared before and after the facial expressions are filled, and the recognition rates of different algorithms are compared. Results: The images generated by the method in this paper are truly coherent and have little impact on facial expression recognition. When the occlusion area is less than 50%, the overall recognition rate of the model is above 80%, which is close to the recognition rate pertaining to the non-occluded images. Conclusions: The experimental results show that the method in this paper has a better restoration effect and higher recognition rate for face images of different occlusion types and regions. Furthermore, it can be used for face recognition in a daily occlusion environment, and achieve a better recognition effect.

Face occlusion removal for face recognition using the related face by structural similarity index measure and principal component analysis

Facial occlusions like sunglasses, masks, caps etc. have severe consequences when reconstructing the partially occluded regions of a facial picture. This paper proposes a novel hybrid machine learning approach for occlusion removal based on Structural Similarity Index Measure (SSIM) and Principal Component Analysis (PCA), called SSIM_PCA. The proposed system comprises two stages. In the first stage, a Face Similar Matrix (FSM) guided by the Structural Similarity Index Measure is generated to provide the necessary information to recover from the lost regions of the face image. The FSM generates Related Face (RF) images similar to the probe image. In the second stage, these RF images are considered as related information and used as input data to generate eigenspaces using PCA to reconstruct the occluded face region exploiting the relationship between the occluded region and related face images, which contain relevant data to recover from the occluded area. Experimental results with three standard datasets viz. Caspeal-R1, IMFDB, and FEI have proven that the proposed method works well under illumination changes and occlusion of facial images.

Selection of Outline Descriptors Based on LightGBM with Application to Infrared Image Target Recognition

Infrared sensing technology can be well used for night observation, which is becoming an important measure for battlefield reconnaissance. It is a powerful way to implement precision strikes and situational awareness by improving the ability of target recognition based on infrared images. For the problem of infrared image recognition, the Light Gradient Boosting Machine (LightGBM) is employed to select the outline descriptors extracted based on the elliptic Fourier series (EFS), which is combined with sparse representation-based classification (SRC) to achieve target recognition. First, based on the target outlines in the infrared image, the multi-order outline descriptors are extracted to characterize the essential characteristics of the target to be recognized. Then, the LightGBM feature selection algorithm is used to screen the multi-order outline descriptors to reduce redundancy and improve the pertinence of features. Finally, the selected outline descriptors are classified based on SRC. The method effectively improves the effectiveness of the final features through the feature selection of LightGBM and reduces the computational complexity of classification at the same time, which is beneficial to improve the overall recognition performance. The mid-wave infrared (MWIR) dataset of various targets is employed to carry out verification experiments for the proposed method under three different conditions of original samples, noisy samples, and partially occluded samples. By comparing the proposed method with several types of existing infrared target recognition methods, the results show that the proposed method can achieve better performance.

Weighted Module Linear Regression Classifications for Partially-Occluded Face Recognition

Face images with partially-occluded areas create huge deteriorated problems for face recognition systems. Linear regression classification (LRC) is a simple and powerful approach for face recognition, of course, it cannot perform well under occlusion situations as well. By segmenting the face image into small subfaces, called modules, the LRC system could achieve some improvements by selecting the best non-occluded module for face classification. However, the recognition performance will be deteriorated due to the usage of the module, a small portion of the face image. We could further enhance the performance if we can properly identify the occluded modules and utilize all the non-occluded modules as many as possible. In this chapter, we first analyze the texture histogram (TH) of the module and then use the HT difference to measure its occlusion tendency. Thus, based on TH difference, we suggest a general concept of the weighted module face recognition to solve the occlusion problem. Thus, the weighted module linear regression classification method, called WMLRC-TH, is proposed for partially-occluded fact recognition. To evaluate the performances, the proposed WMLRC-TH method, which is tested on AR and FRGC2.0 face databases with several synthesized occlusions, is compared to the well-known face recognition methods and other robust face recognition methods. Experimental results show that the proposed method achieves the best performance for recognize occluded faces. Due to its simplicity in both training and testing phases, a face recognition system based on the WMLRC-TH method is realized on Android phones for fast recognition of occluded faces.

A pre-clinical animal study of a novel mechanical-only ablation device treatment for superficial venous disease

Objective Current minimally invasive techniques for ablation in superficial venous reflux are limited to thermal based systems requiring tumescent anesthesia, non-thermal chemical sclerosants and permanent glue implantation. The aim of this feasibility study was to determine the safety and efficacy of a novel mechanical-only ablation (MOA) device called EnVena, in a recognised large animal model with chronic follow up. Methods Venous ablation of six lateral saphenous veins in three sheep was performed using the EnVena device. Luminal patency and vein wall fibrosis were evaluated by histologic analysis at 51 and 89 days. Results All treated veins demonstrated fibrotic occlusion in contiguous segments at 51 and 89 days on histological analysis. From 45 consecutive segments spanning the treatment length across the six treated veins, 26 (57%) were fully occluded, 7 (16%) were impinged or partially occluded and 12 (27%) were open. There were no device related complications during the follow up period. Conclusions A purely mechanical approach to superficial venous ablation demonstrated safety and efficacy in a recognized large animal model based on histological findings.

Action Classification for Partially Occluded Silhouettes by Means of Shape and Action Descriptors

This paper presents an action recognition approach based on shape and action descriptors that is aimed at the classification of physical exercises under partial occlusion. Regular physical activity in adults can be seen as a form of non-communicable diseases prevention, and may be aided by digital solutions that encourages individuals to increase their activity level. The application scenario includes workouts in front of the camera, where either the lower or upper part of the camera’s field of view is occluded. The proposed approach uses various features extracted from sequences of binary silhouettes, namely centroid trajectory, shape descriptors based on the Minimum Bounding Rectangle, action representation based on the Fourier transform and leave-one-out cross-validation for classification. Several experiments combining various parameters and shape features are performed. Despite the presence of occlusion, it was possible to obtain about 90% accuracy for several action classes, with the use of elongation values observed over time and centroid trajectory.

Access to Daylight and Views Improves Physical and Emotional Wellbeing of Office Workers: A Crossover Study

While buildings are designed with expansive windows to allow for abundant daylight and views, in practice, they are frequently occluded to control for heat and glare. Electrochromic glass windows are a solution designed to maximize occupant access to daylight and views throughout the day, and previous research has demonstrated the benefits they can impart on sleep and cognition. We extend this research to quantify its impacts on the perceived physical and emotional wellbeing of occupants in an office environment. 30 office workers spent 5 days working in each of two West-facing offices that were identical with the exception of the window treatment: one with dynamic electrochromic windows and the other with functionally standard windows partially occluded by a fixed roller shade. When working in the office with electrochromic glass, participants were 8-fold more likely of report satisfaction with daylight conditions and 6.5-fold more likely to report satisfaction with views of the outside, were 48% less likely to report eyestrain, perceived a significant benefit to positive affect and were 77% less likely to report feeling depressed. This study adds to the growing body of research demonstrating the importance of daylighting strategies in designing spaces that support the physical and mental health of occupants.