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Most existing trackers based on RGB/grayscale frames may collapse due to the unreliability of conventional sensors in some challenging scenarios (e.g., motion blur and high dynamic range). Event-based cameras as bioinspired sensors encode brightness changes with high temporal resolution and high dynamic range, thereby providing considerable potential for tracking under degraded conditions. Nevertheless, events lack the fine-grained texture cues provided by RGB/grayscale frames. This complementarity encourages us to fuse visual cues from the frame and event domains for robust object tracking under various challenging conditions. In this paper, we propose a novel event feature extractor to capture spatiotemporal features with motion cues from event-based data by boosting interactions and distinguishing alterations between states at different moments. Furthermore, we develop an effective feature integrator to adaptively fuse the strengths of both domains by balancing their contributions. Our proposed module as the plug-in can be easily applied to off-the-shelf frame-based trackers. We extensively validate the effectiveness of eight trackers extended by our approach on three datasets: EED, VisEvent, and our collected frame-event-based dataset FE141. Experimental results also show that event-based data is a powerful cue for tracking. [ABSTRACT FROM AUTHOR]

Jointly exploiting multiple different yet complementary domain information has been proven to be an effective way to perform robust object tracking. This paper focuses on effectively representing and utilizing complementary features from the frame domain and event domain for boosting object tracking performance in challenge scenarios. Specifically, we propose common features extractor to learn potential common representations from the RGB domain and event domain. For learning the unique features of the two domains, we utilize a unique extractor for event based on Spiking neural networks to extract edge cues in the event domain which may be missed in RGB in some challenging conditions, and a unique extractor for RGB based on deep convolutional neural networks to extract texture and semantic information in RGB domain. Extensive experiments on standard RGB benchmark and real event tracking dataset demonstrate the effectiveness of the proposed approach. We show our approach outperforms all compared state-of-the-art tracking algorithms and verify event-based data is a powerful cue for tracking in challenging scenes. [ABSTRACT FROM AUTHOR]

As the stability of surrounding rock of coal roadway is affected by many factors, which makes the classification result hard to be consistent with the field practice. To solve the above problems, this paper proposes a method for the classification of stability of rock which is present in roadway of coal using the artificial intelligence algorithm. In this paper, the influencing factors of stability of rock which is present in roadway are analyzed, and seven influential factors are selected as classification indexes. To solve the problem of slow convergence speed and easy to fall into the local minimum of the back propagation artificial neural network (BP-ANN), an improved BP-ANN algorithm based on additional momentum and Levenberg-Marquardt optimization is proposed based on the analysis of the existing improved methods, which improves the convergence speed and avoids the local minimum effectively. Based on the learning model available, classification system based on fuzzy rule have been implemented and yielded better behavior in the situation of uncertain data sets. Finally, the stability classification model of surrounding rocks of coal roadway using BP-ANN was established in MATLAB environment, and the model was applied to 13 data samples of coal roadway for testing, with the identification rate of 92.3%. The experimental results verify that the method proposed based on fuzzy rule classification system in this paper has a high accuracy of type identification and is applicable to the stability classification of surrounding rock in the coal roadway. [ABSTRACT FROM AUTHOR]