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Robust real-time eye detection and tracking under variable lighting conditions and various face orientations

Video object segmentation and tracking are two essential building blocks of smart surveillance systems. However, there are several issues that need to be resolved. Threshold decision is a difficult problem for video object segmentation with a multi-background model. In addition, some conditions make robust video object tracking difficult. These conditions include nonrigid object motion, target appearance variations due to changes in illumination, and background clutter. In this paper, a video object segmentation and tracking framework is proposed for smart cameras in visual surveillance networks with two major contributions. First, we propose a robust threshold decision algorithm for video object segmentation with a multi-background model. Second, we propose a video object tracking framework based on a particle filter with the likelihood function composed of diffusion distance for measuring color histogram similarity and motion clue from video object segmentation. The proposed framework can track nonrigid moving objects under drastic changes in illumination and background clutter. Experimental results show that the presented algorithms perform well for several challenging sequences, and our proposed methods are effective for the aforementioned issues.

In recent years, tracking of articulated objects at high speed with monocular cameras has been gaining attention. This study presents a novel method for high-frame-rate articulated object tracking with a monocular camera. The method is an extended version of our previous research on high-speed monocular rigid object tracking. In this study, to realize tracking of an articulated object, we integrate dual-quaternion kinematics with our previous fast pixel-wise-posteriors (fast-PWP3D) tracking framework, and propose an auto-regressive (AR) process to encode the dynamic propagation of the estimated state vectors. We give a full three-dimensional derivation of the mathematical formulation of our method and show that our method is capable of tracking an articulated object having a large number of degrees of freedom with only a monocular camera, and is robust against dynamic environmental changes (e.g., illumination/partial occlusion). Moreover, we show an efficient implementation strategy of our method. The results of real-time experiments show that we achieved nearly 350 Hz performance when tracking a four degrees-of-freedom (4-DOF) articulated object with a monocular camera.

Segmenting and tracking of objects in video is of great importance for video-based encoding, surveillance, and retrieval. However, the inherent difficulty of object segmentation and tracking is to distinguish changes in the displacement of objects from disturbing effects such as noise and illumination changes. Therefore, in this paper, we formulate a color-based deformable model which is robust against noisy data and changing illumination. Computational methods are presented to measure color constant gradients. Further, a model is given to estimate the amount of sensor noise through these color constant gradients. The obtained uncertainty is subsequently used as a weighting term in the deformation process. Experiments are conducted on image sequences recorded from three-dimensional scenes. From the experimental results, it is shown that the proposed color constant deformable method successfully finds object contours robust against illumination, and noisy, but homogeneous regions.

Mapping is one of the mobile robot's most basic applications. A mobile robot's sensors, such as a laser sensor, sonar, and camera, are used to create the map. Most mapping techniques use simultaneous localization and Mapping (SLAM). SLAM allows for creating a map and the localization of the robot's position on it. This research compares the trajectories of a mobile robot created by several ROS-based SLAM systems. And also, GMapping and Karto SLAM are two well-known SLAM algorithms employed. The mobile robot is equipped with 2D lidar and monocular camera. The mapping is done at two distinct locations, in labs of varying sizes with varying numbers of static and dynamic objects. Three test runs are conducted for GMapping to examine the effects of various variables on mapping quality, including particle filter, mapping delay, and robot speed. The results show a significant difference in operation completion time and mapping accuracy as a result of the parameter changing over the three test runs. Due to the improved accuracy of the parameter used in the second test run of GMapping and Karto SLAM, the accuracy of the maps is the basis for this improvement. On the other hand, the second test run with robot particle filter 30, mapping delay 1, and speed 0.13m/s is thought to be the best3\2 13Q/0.

Robust visual tracking framework in the presence of blurring by arbitrating appearance- and feature-based detection

We present an on-the-move LIDAR-based object detection system for autonomous and semi-autonomous unmanned vehicle systems. In this paper we make several contributions: (i) we describe an algorithm for real-time detection of objects such as doors and stairs in indoor environments; (ii) we describe efficient data structures and algorithms for processing 3D point clouds acquired by laser scanners in a streaming manner, which minimize the memory copying and access. We show qualitative results demonstrating the effectiveness of our approach on runs in an indoor office environment.

In this work we consider a mobile robot with a laser range finder. Our goal is to find the best set of lines from the sequence of points given by a laser scan. We propose a probabilistic method to deal with noisy laser scans, in which the noise is not properly modeled using a Gaussian Distribution. An experimental comparison with a very well known method (SMSM), using a mobile robot simulator and a real mobile robot, shows the robustness of the new method. The new method is also fast enough to be used in real time.

In this paper, we present our autonomous mobile robotic system for surveillance of indoor environments. The robot is equipped with a monocular camera, a laser scanner, encoders, and an RFID device, connected to a multi-layer decision and control scheme. Two main functions are implemented: to build a map of the environment, identifying specific areas of interest, marked by RFID tags; to monitor the target zones to detect unexpected changes, such as object addition or removal, based on a vision scene change detector (V-SCD) and a laser scene change detector (L-SCD). Fuzzy logic is used to integrate information provided by the different sensors. Applications of the proposed system include surveillance of wide areas, such as airports and museums, and monitoring of safety equipment. The feasibility of the approach is demonstrated through experimental tests, performed in the ISSIA Mobile Robotics Laboratory of Ban, Italy. The results are promising, proving the method to be effective in detecting either new or removed objects in the surveyed scene, also in presence of relatively small viewpoint changes. It is shown that the proposed robotic surveillance system successfully addresses a number of specific problems related to environment mapping, autonomous navigation, and scene processing, and can be potentially employed in real world surveillance applications.

Object tracking tasks often face problems such as illumination and appearance changes due to various scenarios. To effectively solve such problems, single object tracking algorithms in recent years have improved the stability of the tracker by introducing online learning, reducing tracking drift, and tracking objects stably even with large deformations. However, on the one hand, employing online learning increases computation costs, causing the tracker to run slower. On the other hand, because it learns from the tracking results, the tracking template will only be correctly updated if the object is accurately tracked. Any incorrect update will result in tracking failure. In this paper, we propose a filtering algorithm for online learning sample processing in object tracking to remedy these issues. Our method can select valid training samples and discard unnecessary training samples in object tracking. Numerous experiments demonstrate that our algorithm improves the tracking accuracy, reduces the computation complexity, and improves the running speed and run‐time. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Tracking objects or humans using background subtraction is commonly used both in indoor and outdoor environment. But this method may not produce quality results due to illumination change, shadows, and occlusion occurred in indoor or outdoor environments. Song and Nevatia suggested a simultaneous tracking of face and body for human tracking in an indoor environment using Adaboost face detection and Camshift color tracking methods (2004). Changes in illumination are not considered in their work. In this research, an intelligent method for color tracking scheme using skin reflectance values is proposed to track the human in real-time video for varying illuminations both in indoor and outdoor environments. This research finds application in the live transmission of an indoor video conference or a live news cast from an outdoor as well as in the places where the illumination change occurs frequently

Robot navigation allows robot mobility. Therefore, mobility is an area of robotics that has been actively investigated since robots were first developed. In recent years, interest in personal service robots for homes and public facilities has increased. As a result, robot navigation within the home environment, which is an indoor environment, is being actively investigated. However, the problem with conventional navigation algorithms is that they require a large computation time for their building mapping and path planning processes. This problem makes it difficult to cope with an environment that changes in real-time. Therefore, we propose a humanoid robot navigation algorithm consisting of an image processing and optimization algorithm. This algorithm realizes navigation with less computation time than conventional navigation algorithms using map building and path planning processes, and can cope with an environment that changes in real-time.

Tracking objects over fixed surveillance cameras are widely used for security purposes in public areas such as train stations, airports, parking areas, and public transportation for the prevention of terrorism. Once the object is accurately detected in the image scene, we can use various visual algorithms to find a number of applications. In this paper, we introduce a model for tracking the multiple objects along with detecting the abandoned luggage in the real time environment. In our model, we used the initial frames to model the background scene. Next, we used the motion model that is background subtraction to detect and track moving objects such as the owner and the luggage. The proposed model also maintains the position history of moving objects followed by the frame differencing technique to find out the luggage history and detect the abandoned luggage by a human. We have used PETS2006 and PETS2007 dataset for the testing of the proposed system in various indoor and outdoor environments with varying lighting conditions.

This thesis presents a recent research on the problem of environmental modeling for both localization and map building for wheel-based, differential driven, fully autonomous and self-contained mobile robots. The robots behave in an indoor office environment. They have a multi-sensor setup where the encoders are used for odometry and two exteroperceptive sensors, a 360° laser scanner and a monocular vision system, are employed to perceive the surrounding. The whole approach is feature based meaning that instead of directly using the raw data from the sensor features are firstly extracted. This allows the filtering of noise from the sensors and permits taking account of the dynamics in the environment. Furthermore, a properly chosen feature extraction has the characteristic of better isolating informative patterns. When describing these features care has to be taken that the uncertainty from the measurements is taken into account. The representation of the environment is crucial for mobile robot navigation. The model defines which perception capabilities are required and also which navigation technique is allowed to be used. The presented environmental model is both metric and topological. By coherently combining the two paradigms the advantages of both methods are added in order to face the drawbacks of a single approach. The capabilities of the hybrid approach are exploited to model an indoor office environment where metric information is used locally in structures (rooms, offices), which are naturally defined by the environment itself while the topology of the whole environment is resumed separately thus avoiding the need of global metric consistency. The hybrid model permits the use of two different and complementary approaches for localization, map building and planning. This combination permits the grouping of all the characteristics which enables the following goals to be met: Precision, robustness and practicability. Metric approaches are, per definition, precise. The use of an Extended Kalman Filter (EKF) permits to have a precision which is just bounded by the quality of the sensor data. Topological approaches can easily handle large environments because they do not heavily rely on dead reckoning. Global consistency can, therefore, be maintained for large environments. Consistent mapping, which handle large environments, is achieved by choosing a topological localization approach, based on a Partially Observable Markov Decision Process (POMDP), which is extended to simultaneous localization and map building. The theory can be mathematically proven by making some assumptions. However, as stated during the whole work, at the end the robot itself has to show how good the theory is when used in the real world. For this extensive experimentation for a total of more than 9 km is performed with fully autonomous self-contained robots. These experiments are then carefully analyzed. With the metric approach precision with error bounds of about 1 cm and less than 1 degree is further confirmed by ground truth measurements with a mean error of less than 1 cm. The topological approach is successfully tested by simultaneous localization and map building where the automatically created maps turned out to work better than the a priori maps. Relocation and closing the loop are also successfully tested.

Object detection and tracking under complex environment are challenging because of the disturbances induced by background clutter, illumination changes, occlusions and other factors. The bulk of traditional algorithms basically rely on hand‐crafted features, which are not sufficiently robust to a complex environment. Moreover, the processes of detection and tracking are separated, which leads to the overall efficiency not high. In this study, a novel local probability model (LPM)‐based mean shift (MS) algorithm is proposed to integrate object detection and tracking. The main contributions include: (i) a new framework based on the combination of LPM and MS is established for the integration of object tracking and detection. (ii) For object detection, the training and prediction of LPM are built by stacked denoising autoencoders based deep learning. (iii) For object tracking, an MS tracking algorithm leveraging LPM is modified to improve the tracking efficiency under a complex environment. Experimental results demonstrate that the proposed method is superior to the colour histograms based MS and histograms of oriented gradients based MS in terms of robustness and tracking accuracy.