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Identification Vehicle Movement Detection in Forest Area using MFCC and KNN

机译:使用MFCC和KNN识别森林面积的识别车辆运动检测

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Audio-based monitoring systems have proven to be very useful tools for detecting such cases like gunshot detection, noise assessment in urban areas etc. There are several existing acoustic event detection algorithm that helps in different applications for area monitoring/ security concerns. It might normally be the case that there is hand saw/ axe / tiger roaring / animal call / vehicle engine sound from these kinds of incidents. Acoustic sensing at timely and correctly detection of the events is the main objective of event detection system. Timber theft or animal killing are the cases which should be identify and taken action by the security officers in the forest and this is only possible by image or acoustic sound detection. Image processing becomes too costly and not possible for wide area coverage but acoustic sensors are cheaper and effective. Automatic recognition of sound events can be valuable for efficient situations examination of acoustic scene. In this paper we concentrate on the problem of detecting the unauthorized vehicle movement in forest area. The primary goal is the continuous acoustic surveillance for a particular natural scene for illegal human activities (trespassers, killing animals etc.) in order to promptly alert an authorized officer for taking the appropriate measures.
机译:基于音频的监控系统已被证明是用于检测枪门检测,噪声评估等枪支检测等案例的非常有用的工具,有几种现有的声学事件检测算法有助于区域监测/安全问题的不同应用。通常是这种情况的手锯/斧头咆哮/动物呼叫/车辆发动机声音的情况。及时和正确检测事件的声学感应是事件检测系统的主要目标。木材盗窃或动物杀戮是应由森林中的安全官员识别和采取行动的情况,这仅是通过图像或声学声音检测的。图像处理变得过于昂贵,而且广泛的面积覆盖率不可能,但声学传感器更便宜,有效。自动识别声音事件对于声学场景的有效情况来说是有价值的。在本文中,我们专注于检测森林面积未经授权的车辆运动的问题。主要目标是非法人类活动(侵入者,杀害动物等)的特定自然场景的连续声监控,以便及时警惕授权人员采取适当措施。

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