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Most cameras on the market today, incorporate some built-in detection analytics. These are referred to as ‘edge’ analytics. Edge analytics are often suitable for indoor detection, under controlled lighting conditions and without environmental interference. Some may even be suitable for simple outdoor detection, over shorter distances.
However most outdoor environments, pose big challenges to these edge device analytics. Poor lighting, camera shake/vibration, adverse weather conditions, presence of bugs, vegetation clutter, are all very common factors which may generate false alarms.
In order to overcome these challenges without compromising the detection accuracy, it is advisable to rely on the most advanced server-based, detection analytics technologies. For manufacturers is it faster to develop, and easier to deploy improvements to their algorithms in a server-based architecture. And server-based architectures are capable of delivering much more computational power, which enables the use of the latest, most advanced analytics algorithms based on deep learning/artificial intelligence.
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