The combination of a video camera with an acoustic vector sensor (AVS) opens new possibilities in environment awareness applications. The goal of this thesis is the design of an algorithm for detection and tracking of low-flying aircraft using a multi-coordinate VAUDEO system. A commercial webcam placed in line with an AVS in a ground array are used to record real low-flying aircraft data at Teuge international airport. Each frame, the algorithm analyzes a matrix of three orthogonal acoustic particle velocity signals and one acoustic pressure signal using the Singular Value Decomposition to estimate the Direction of Arrival, DoA of propeller aircraft sound. The DoA data is then applied to a Kalman filter and its output is used later on to narrow the region of video frame processed. Background subtraction is applied followed by a Gaussian-weighted intensity mask to assign high priority to moving objects which are closer to the sound source estimated position. The output is applied to another Kalman filter to improve the accuracy of the aircraft location estimation. The performance evaluation of the algorithm proved that it is comparable to the performances of state-of-the-art video alone based algorithms. In conclusion, the combination of video and directional audio increases the accuracy of propeller aircraft detection and tracking comparing to reported previous work using audio alone. / +593 980826278