The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative. Results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis and a "real-time" experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. A long-term tracking subchallenge has been introduced to the set of standard VOT sub-challenges. The new subchallenge focuses on long-term tracking properties, namely coping with target disappearance and reappearance. A new dataset has been compiled and a performance evaluation methodology that focuses on long-term tracking capabilities has been adopted. The VOT toolkit has been updated to support both standard short-term and the new longterm tracking subchallenges. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website 60 .

We took panoramic snapshots in outdoor scenes at regular intervals in two- or three-dimensional grids covering 1 m2 or 1 m3 and determined how the root mean square pixel differences between each of the images and a reference image acquired at one of the locations in the grid develop over distance from the reference position. We then asked whether the reference position can be pinpointed from a random starting position by moving the panoramic imaging device in such a way that the image differences relative to the reference image are minimized. We find that on time scales of minutes to hours, outdoor locations are accurately defined by a clear, sharp minimum in a smooth three-dimensional (3D) volume of image differences (the 3D difference function). 3D difference functions depend on the spatial-frequency content of natural scenes and on the spatial layout of objects therein. They become steeper in the vicinity of dominant objects. Their shape and smoothness, however, are affected by changes in illumination and shadows. The difference functions generated by rotation are similar in shape to those generated by translation, but their plateau values are higher. Rotational difference functions change little with distance from the reference location. Simple gradient descent methods are surprisingly successful in recovering a goal location, even if faced with transient changes in illumination. Our results show that view-based homing with panoramic images is in principle feasible in natural environments and does not require the identification of individual landmarks. We discuss the relevance of our findings to the study of robot and insect homing.

Freely flying bees were filmed as they landed on a flat, horizontal surface, to investigate the underlying visuomotor control strategies. The results reveal that (1) landing bees approach the surface at a relatively shallow descent angle; (2) they tend to hold the angular velocity of the image of the surface constant as they approach it; and (3) the instantaneous speed of descent is proportional to the instantaneous forward speed. These characteristics reflect a surprisingly simple and effective strategy for achieving a smooth landing, by which the forward and descent speeds are automatically reduced as the surface is approached and are both close to zero at touchdown. No explicit knowledge of flight speed or height above the ground is necessary. A model of the control scheme is developed and its predictions are verified. It is also shown that, during landing, the bee decelerates continuously and in such a way as to keep the projected time to touchdown constant as the surface is approached. The feasibility of this landing strategy is demonstrated by implementation in a robotic gantry equipped with vision.

Hand gestures are a form of nonverbal communication that can be used in several fields such as communication between deaf-mute people, robot control, human–computer interaction (HCI), home automation and medical applications. Research papers based on hand gestures have adopted many different techniques, including those based on instrumented sensor technology and computer vision. In other words, the hand sign can be classified under many headings, such as posture and gesture, as well as dynamic and static, or a hybrid of the two. This paper focuses on a review of the literature on hand gesture techniques and introduces their merits and limitations under different circumstances. In addition, it tabulates the performance of these methods, focusing on computer vision techniques that deal with the similarity and difference points, technique of hand segmentation used, classification algorithms and drawbacks, number and types of gestures, dataset used, detection range (distance) and type of camera used. This paper is a thorough general overview of hand gesture methods with a brief discussion of some possible applications.

A family of reflective surfaces is presented that, when imaged by a camera, can capture a global view of the visual environment. By using these surfaces in conjunction with conventional imaging devices, it is possible to produce fields of view in excess of 180 degrees that are not affected by the distortions and aberrations found in refractive wide-angle imaging devices. By solving a differential equation expressing the camera viewing angle as a function of the angle of incidence on a reflective surface, a family of appropriate surfaces has been derived. The surfaces preserve a linear relationship between the angle of incidence of light onto the surface and the angle of reflection onto the imaging device, as does a normal mirror. However, the gradient of this linear relationship can be varied as desired to produce a larger or smaller field of view. The resulting family of surfaces has a number of applications in surveillance and machine vision.