In recent years, there has been a surge in interest in a few features of high-tech commercial drones. Small drones have a higher potential for being used for illegal operations due to their ability to evade ground security while transporting payloads. Security breaches like this may only be avoided with drone tracking and monitoring. Recognizing drones in surveillance footage can be challenging due to the similarity between small drones and birds, especially against complex backdrops. Keeping an eye out for drones and other flying objects manually is a time-consuming and difficult task. Therefore, it is necessary to employ a mechanical means of telling drones apart from birds. In this research, we create a system for drone identification using focus measure operators (FMOs). On every video frame, the five FMO parameters are calculated. Drone identification begins with a feature ranking to determine which features are most important and then continues with a classification using a random forest (RF) classifier. The Workshop on Small-Drone Surveillance, Detection, and Counteraction Techniques (WOSDETC), with funding from the Safe Shore Consortium, provides the data used to assess the suggested method’s efficacy in the Drone-vs-Bird Detection Challenge at IEEE AVSS2021. The suggested method presents to identify drones with drone present (DP) vs neither drone nor bird present (NDNBP) (two class), DP vs both bird and drone present (BBDP) vs NDNBP (three class), DP vs BP vs BBDP vs NDNBP (four class) with average acc 94.15%with sensitivity 96.69%, acc 93.60% with sensitivity 96.20%, acc 92% with sensitivity 95%, respectively for Moving Camera (MC) recordings. For drone identification, the average acc for a two-, three-, and four-class classification method using a moving and a stationary camera is 96.24%, 94.12%, and 95%, respectively.