The Evolution of First Person Vision Methods: A Survey

The emergence of new wearable technologies such as action cameras and smart-glasses has increased the interest of computer vision scientists in the First Person perspective. Nowadays, this field is attracting attention and investments of companies aiming to develop commercial devices with First Person Vision recording capabilities. Due to this interest, an increasing demand of methods to process these videos, possibly in real-time, is expected. Current approaches present a particular combinations of different image features and quantitative methods to accomplish specific objectives like object detection, activity recognition, user machine interaction and so on. This paper summarizes the evolution of the state of the art in First Person Vision video analysis between 1997 and 2014, highlighting, among others, most commonly used features, methods, challenges and opportunities within the field.Comment: First Person Vision, Egocentric Vision, Wearable Devices, Smart Glasses, Computer Vision, Video Analytics, Human-machine Interactio

Computer vision in target pursuit using a UAV

Research in target pursuit using Unmanned Aerial Vehicle (UAV) has gained attention in recent years, this is primarily due to decrease in cost and increase in demand of small UAVs in many sectors. In computer vision, target pursuit is a complex problem as it involves the solving of many sub-problems which are typically concerned with the detection, tracking and following of the object of interest. At present, the majority of related existing methods are developed using computer simulation with the assumption of ideal environmental factors, while the remaining few practical methods are mainly developed to track and follow simple objects that contain monochromatic colours with very little texture variances. Current research in this topic is lacking of practical vision based approaches. Thus the aim of this research is to fill the gap by developing a real-time algorithm capable of following a person continuously given only a photo input. As this research considers the whole procedure as an autonomous system, therefore the drone is activated automatically upon receiving a photo of a person through Wi-Fi. This means that the whole system can be triggered by simply emailing a single photo from any device anywhere. This is done by first implementing image fetching to automatically connect to WIFI, download the image and decode it. Then, human detection is performed to extract the template from the upper body of the person, the intended target is acquired using both human detection and template matching. Finally, target pursuit is achieved by tracking the template continuously while sending the motion commands to the drone. In the target pursuit system, the detection is mainly accomplished using a proposed human detection method that is capable of detecting, extracting and segmenting the human body figure robustly from the background without prior training. This involves detecting face, head and shoulder separately, mainly using gradient maps. While the tracking is mainly accomplished using a proposed generic and non-learning template matching method, this involves combining intensity template matching with colour histogram model and employing a three-tier system for template management. A flight controller is also developed, it supports three types of controls: keyboard, mouse and text messages. Furthermore, the drone is programmed with three different modes: standby, sentry and search. To improve the detection and tracking of colour objects, this research has also proposed several colour related methods. One of them is a colour model for colour detection which consists of three colour components: hue, purity and brightness. Hue represents the colour angle, purity represents the colourfulness and brightness represents intensity. It can be represented in three different geometric shapes: sphere, hemisphere and cylinder, each of these shapes also contains two variations. Experimental results have shown that the target pursuit algorithm is capable of identifying and following the target person robustly given only a photo input. This can be evidenced by the live tracking and mapping of the intended targets with different clothing in both indoor and outdoor environments. Additionally, the various methods developed in this research could enhance the performance of practical vision based applications especially in detecting and tracking of objects

Recognition of Activities of Daily Living with Egocentric Vision: A Review.

Video-based recognition of activities of daily living (ADLs) is being used in ambient assisted living systems in order to support the independent living of older people. However, current systems based on cameras located in the environment present a number of problems, such as occlusions and a limited field of view. Recently, wearable cameras have begun to be exploited. This paper presents a review of the state of the art of egocentric vision systems for the recognition of ADLs following a hierarchical structure: motion, action and activity levels, where each level provides higher semantic information and involves a longer time frame. The current egocentric vision literature suggests that ADLs recognition is mainly driven by the objects present in the scene, especially those associated with specific tasks. However, although object-based approaches have proven popular, object recognition remains a challenge due to the intra-class variations found in unconstrained scenarios. As a consequence, the performance of current systems is far from satisfactory

Visual Attention Mechanism for a Social Robot

This paper describes a visual perception system for a social robot. The central part of this system is an artificial attention mechanism that discriminates the most relevant information from all the visual information perceived by the robot. It is composed by three stages. At the preattentive stage, the concept of saliency is implemented based on ‘proto-objects’ [37]. From these objects, different saliency maps are generated. Then, the semiattentive stage identifies and tracks significant items according to the tasks to accomplish. This tracking process allows to implement the ‘inhibition of return’. Finally, the attentive stage fixes the field of attention to the most relevant object depending on the behaviours to carry out. Three behaviours have been implemented and tested which allow the robot to detect visual landmarks in an initially unknown environment, and to recognize and capture the upper-body motion of people interested in interact with it

Robust pedestrian detection and tracking in crowded scenes

In this paper, a robust computer vision approach to detecting and tracking pedestrians in unconstrained crowded scenes is presented. Pedestrian detection is performed via a 3D clustering process within a region-growing framework. The clustering process avoids using hard thresholds by using bio-metrically inspired constraints and a number of plan view statistics. Pedestrian tracking is achieved by formulating the track matching process as a weighted bipartite graph and using a Weighted Maximum Cardinality Matching scheme. The approach is evaluated using both indoor and outdoor sequences, captured using a variety of different camera placements and orientations, that feature significant challenges in terms of the number of pedestrians present, their interactions and scene lighting conditions. The evaluation is performed against a manually generated groundtruth for all sequences. Results point to the extremely accurate performance of the proposed approach in all cases

Spatiotemporal Saliency Detection: State of Art

Saliency detection has become a very prominent subject for research in recent time. Many techniques has been defined for the saliency detection.In this paper number of techniques has been explained that include the saliency detection from the year 2000 to 2015, almost every technique has been included.all the methods are explained briefly including their advantages and disadvantages. Comparison between various techniques has been done. With the help of table which includes authors name,paper name,year,techniques,algorithms and challenges. A comparison between levels of acceptance rates and accuracy levels are made

Multigranularity Representations for Human Inter-Actions: Pose, Motion and Intention

Tracking people and their body pose in videos is a central problem in computer vision. Standard tracking representations reason about temporal coherence of detected people and body parts. They have difficulty tracking targets under partial occlusions or rare body poses, where detectors often fail, since the number of training examples is often too small to deal with the exponential variability of such configurations. We propose tracking representations that track and segment people and their body pose in videos by exploiting information at multiple detection and segmentation granularities when available, whole body, parts or point trajectories. Detections and motion estimates provide contradictory information in case of false alarm detections or leaking motion affinities. We consolidate contradictory information via graph steering, an algorithm for simultaneous detection and co-clustering in a two-granularity graph of motion trajectories and detections, that corrects motion leakage between correctly detected objects, while being robust to false alarms or spatially inaccurate detections. We first present a motion segmentation framework that exploits long range motion of point trajectories and large spatial support of image regions. We show resulting video segments adapt to targets under partial occlusions and deformations. Second, we augment motion-based representations with object detection for dealing with motion leakage. We demonstrate how to combine dense optical flow trajectory affinities with repulsions from confident detections to reach a global consensus of detection and tracking in crowded scenes. Third, we study human motion and pose estimation. We segment hard to detect, fast moving body limbs from their surrounding clutter and match them against pose exemplars to detect body pose under fast motion. We employ on-the-fly human body kinematics to improve tracking of body joints under wide deformations. We use motion segmentability of body parts for re-ranking a set of body joint candidate trajectories and jointly infer multi-frame body pose and video segmentation. We show empirically that such multi-granularity tracking representation is worthwhile, obtaining significantly more accurate multi-object tracking and detailed body pose estimation in popular datasets