ENHANCING AUTONOMOUS DRONE DELIVERY SYSTEMS: A HYBRID CNN-LSTM APPROACH FOR ROBUST OBJECT TRACKING IN DYNAMIC ENVIRONMENTS

Strong object tracking is needed in autonomous drone delivery to manage changing surroundings appropriately. This research introduces an innovative method that uses CNNs to extract features and LSTM networks to predict the sequence in which objects appear in videos. The primary goal is to enhance the tracking system's reliability while objects move and are hidden and when the environment is unpredictable. We measured the precision, success rate, real-time FPS, and occlusion handling of the method with the help of the MOT Challenge and our custom drone flight dataset. Experiments found that the Proposed Model is more accurate than SiamFC, DeepSORT, and CNN + Kalman Filter, with a precision of 94% and a success rate of 92%, and can operate at 30 FPS in live tests. The model is trusted for uncrewed drone operations because it can overcome occlusions and accurately restore missing parts. This approach improves how autonomous drones navigate, making them useful for logistics, keeping watch, and handling emergencies. The primary contribution of this work lies in the development of a hybrid CNN–LSTM architecture integrated with sensor fusion for robust real-time object tracking. The proposed approach advances existing solutions by improving tracking accuracy, occlusion recovery, and real-time performance in dynamic drone delivery environments.