Robots for precision farming have the potential to reduce the reliance on herbicides and pesticides through selectively spraying individual plants or through manual weed removal. To achieve this, the value crops and the weeds must be identified by the robot's perception system to trigger the actuators for spraying or removal. In this paper, we address the problem of detecting the sugar beet plants as well as weeds using a camera installed on a mobile robot operating on a field. We propose a system that performs vegetation detection, feature extraction, random forest classification, and smoothing through a Markov random field to obtain an accurate estimate of the crops and weeds. We implemented and thoroughly evaluated our system on a real farm robot on different sugar beet fields and illustrate that our approach allows for accurately identifying the weed on the field.

We investigate visual task solution strategies when exploring traditional, orthogonal, and radial node-link tree layouts, four orientations of the non-radial layouts, as well as varying difficulty of the task. The strategies are identified by examining eye movement data recorded in a controlled user study previously conducted by Burch et al. For detailed analysis of the spatio-temporal structures and patterns in the eye tracking data, we employ visual analytics techniques adopted from related methodology for geographic movement data by Andrienko et al. In this way, we complement the statistical analysis of task completion times and error rates reported by Burch et al. with spatio-temporal strategies that explain the variation in completion times. We identify differences between task solution strategies dependent on layout type, orientation, and task difficulty. Furthermore, we examine differences between groups of participants split according to completion time. Our analysis identifies that for all layouts it took nearly the same time to find the task solution node, but in the radial layout the solution was not confirmed directly. Instead, a more frequent cross-checking occurs afterwards, which is the main reason for the impaired performance of radial layouts.

Abstract We investigate four different variants of attention‐guiding video visualization techniques that aim to help users distribute their attention equally among potential objects of interest: bounding box visualization, force‐directed visualization, top‐down visualization, grid visualization. Objects of interest are highlighted by rectangular shapes and then we concentrate on the manipulation of color, motion, and size. We conducted a controlled laboratory user study (n=25) to compare the four visualization techniques and the unmodified video material as baseline. We evaluated task performance and distribution of attention in a search task. These two properties become especially important when video material with numerous objects has to be observed. The distribution of attention was measured by eye tracking. Our results show that a more even distribution of attention between the objects can be achieved by attention‐guiding visualization, compared to unmodified video. Many participants feel more comfortable when they look at bounding boxes and the grid, but improvements in search task performance could not be confirmed.

We present a scalable technique to explore surveillance videos by scatter/gather browsing of trajectories of moving objects. Trajectories are clustered according to a variety of properties, such as location, orientation, and velocity that can be selected by the users. These properties allow for faceted video exploration and refinement of previous browsing steps. The proposed approach facilitates interactive clustering of trajectories by an effective way of cluster visualization that we term schematic summaries. This novel visualization illustrates cluster summaries in a schematic, nonphotorealistic style. To reduce visual clutter, we introduce the trajectory bundling technique. Further, schematic summaries include a timeline view and a showcase view to represent the facets present in a cluster. The fusion of schematic summaries, a variety of facets, and user interaction lead to efficient hierarchical exploration of video data. Examples of different browsing scenarios and initial user feedback demonstrate the potentials of our method.

Abstract In recent years, a collection of new techniques which deal with video as input data, emerged in computer graphics and visualization. In this survey, we report the state of the art in video‐based graphics and video visualization. We provide a review of techniques for making photo‐realistic or artistic computer‐generated imagery from videos, as well as methods for creating summary and/or abstract visual representations to reveal important features and events in videos. We provide a new taxonomy to categorize the concepts and techniques in this newly emerged body of knowledge. To support this review, we also give a concise overview of the major advances in automated video analysis, as some techniques in this field (e.g. feature extraction, detection, tracking and so on) have been featured in video‐based modelling and rendering pipelines for graphics and visualization.

We evaluate and compare video visualization techniques based on fast-forward. A controlled laboratory user study (n = 24) was conducted to determine the trade-off between support of object identification and motion perception, two properties that have to be considered when choosing a particular fast-forward visualization. We compare four different visualizations: two representing the state-of-the-art and two new variants of visualization introduced in this paper. The two state-of-the-art methods we consider are frame-skipping and temporal blending of successive frames. Our object trail visualization leverages a combination of frame-skipping and temporal blending, whereas predictive trajectory visualization supports motion perception by augmenting the video frames with an arrow that indicates the future object trajectory. Our hypothesis was that each of the state-of-the-art methods satisfies just one of the goals: support of object identification or motion perception. Thus, they represent both ends of the visualization design. The key findings of the evaluation are that object trail visualization supports object identification, whereas predictive trajectory visualization is most useful for motion perception. However, frame-skipping surprisingly exhibits reasonable performance for both tasks. Furthermore, we evaluate the subjective performance of three different playback speed visualizations for adaptive fast-forward, a subdomain of video fast-forward.

We introduce a novel technique for visualizing dense time-varying directed and weighted multi-graphs with an additional hierarchical organization of the graph nodes. Combining Indented Tree Plots and TimeRadarTrees, we show the temporal evolution of relations in a static view. The graph edges are layered around thumbnail wheels consisting of color-coded sectors that are representatives of the graph nodes. These sectors generate implicit representations of graph edges. Start and target vertices are perceived by inspecting the color coding of sectors in the context of other sectors and their orientations. The technique puts emphasis on newer relations and hence, these are mapped to a larger display space in the radial diagram. The benefit of our technique is reduction of visual clutter from which node-link diagrams typically suffer. The visualization focuses on an easy exploration of trends, countertrends, periodicity, temporal shifts, and anomalies in time-varying relational data. We demonstrate the usefulness of the approach by applying it to dense dynamic graph data acquired from a soccer match of the 2D Soccer Simulation League.

We present a new and scalable technique to explore surveillance videos by scatter/gather browsing of trajectories of moving objects. The proposed approach facilitates interactive clustering of trajectories by an effective way of cluster visualization that we term schematic summaries. This novel visualization illustrates cluster summaries in a schematic, non-photorealistic style. To reduce visual clutter, we introduce the trajectory bundling technique. The fusion of schematic summaries and user interaction leads to efficient hierarchical exploration of video data. Examples of different browsing scenarios demonstrate the effectiveness of the proposed method.

Node-link diagrams are an effective and popular visualization approach for depicting hierarchical structures and for showing parent-child relationships. In this paper, we present the results of an eye tracking experiment investigating traditional, orthogonal, and radial node-link tree layouts as a piece of empirical basis for choosing between those layouts. Eye tracking was used to identify visual exploration behaviors of participants that were asked to solve a typical hierarchy exploration task by inspecting a static tree diagram: finding the least common ancestor of a given set of marked leaf nodes. To uncover exploration strategies, we examined fixation points, duration, and saccades of participants' gaze trajectories. For the non-radial diagrams, we additionally investigated the effect of diagram orientation by switching the position of the root node to each of the four main orientations. We also recorded and analyzed correctness of answers as well as completion times in addition to the eye movement data. We found out that traditional and orthogonal tree layouts significantly outperform radial tree layouts for the given task. Furthermore, by applying trajectory analysis techniques we uncovered that participants cross-checked their task solution more often in the radial than in the non-radial layouts.

Abstract We present a feasibility study on using video visualization to aid snooker skill training. By involving the coaches and players in the loop of intelligent reasoning, our approach addresses the difficulties of automated semantic reasoning, while benefiting from mature video processing techniques. This work was conducted in conjunction with a snooker club and a sports scientist. In particular, we utilized the principal design of the VideoPerpetuoGram (VPG) to convey spatiotemporal information to the viewers through static visualization, removing the burden of repeated video viewing. We extended the VPG design to accommodate the need for depicting multiple video streams and respective temporal attribute fields, including silhouette extrusion, spatial attributes, and non‐spatial attributes. Our results and evaluation have shown that video visualization can provide snooker coaching with visually quantifiable and comparable summary records, and is thus a cost‐effective means for assessing skill levels and monitoring progress objectively and consistently.