Object classification method comprising: a first sensor 212, detecting an object at a first time (12, Fig. 1); determining a property of the object’s motion; predicting, from the property, the object’s location at a second time (12A, Fig. 1); a second sensor 214 to obtaining further, high resolution, object information at the second time in the predicted location; and classifying the object in dependence thereon. The first sensor may be a stationary fixed field of view (FOV) camera. The second sensor may be a pan-tilt-zoom (PTZ) camera. The sensors may comprise a radar antenna. The second time may be dependent on an adjustment of the sensors. The detected property may be a: location; displacement; velocity; and/or acceleration. A plurality of properties may be detected at different times. An image of the object, obtained at the second time, may be classified by a neural network to determine object: type; size; make and/or model; risk; and confidence score. When detecting a plurality of objects, they may be sorted by drone likelihood and/or threat level; the property of the highest ranked object determined first. The object may be determined to be a drone based on the property or a determined trajectory.
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