A method of automatically analysing video streams captured by a plurality of video surveillance cameras, said cameras each having at least one integrated processor adapted to perform calculations and a capacity for connection to a communications network, said method comprising: - a first video stream analysis processing part (40, 42, 44) implementing a first neural sub-network, performed by each video surveillance camera (2 1, 2,2 N) on at least some of the images forming a video stream captured by said camera implemented by said at least one integrated processor (8, 12) of each video surveillance camera, said first analysis processing part comprising an extraction of first metadata (M, 45), comprising descriptors characteristic of the processed images, by the first neural sub-network, - transmission of said first metadata to at least one remote computer system comprising a plurality of calculation processors, - a second video stream analysis processing part (46, 48, 50, 52) implementing a second neural subnetwork (R 2), implemented by said remote computer system, comprising an extraction of second metadata characteristic of the processed images, using said first metadata, and a factorisation of the second metadata said factorisation consisting in applying a joint processing of second metadata obtained for processed images from at least two distinct video surveillance cameras, in order to perform at least one situation analysis associated with the video streams, and a transmission of the situation analysis result (A j) by the remote computer system to a control centre, the method further including a prior step of determining said first sub-network of neurons (R 1) and said second sub-network of neurons (R 2) from from a deep neural network comprising a plurality of hierarchical depth levels, the said determination comprising the calculation of a deciphering depth level, the first neural sub-network (R 1) implementing the depth levels of said neural network lower than said slicing depth level, the second sub-network of neurons (R 2) implementing the depth levels of said neural network higher than said slicing depth level, in which, for a plurality of tasks to be executed by said deep neural network, said calculation of a slicing depth level is performed, for each video surveillance camera, as a function of calculation capacities of said at least one processor integrated into said video surveillance camera, of the computing capacities of said remote computer system, and of at least one performance criterion calculated for each task to be executed, the computing capacity being estimated by a task execution time, the calculation of a slicing depth level also being a function of a processing frequency associated with each task to be executed. - a first video stream analysis processing part (40, 42, 44) implementing a first neural sub-network, performed by each video surveillance camera (2 1, 2,2 N) on at least some of the images forming a video stream captured by said camera implemented by said at least one integrated processor (8, 12) of each video surveillance camera, said first analysis processing part comprising an extraction of first metadata (M, 45), comprising descriptors characteristic of the processed images, by the first neural sub-network, - transmission of said first metadata to at least one remote computer system comprising a plurality of calculation processors, - a second video stream analysis processing part (46, 48, 50, 52) implementing a second neural subnetwork (R 2), implemented by said remote computer system, comprising an extraction of second metadata characteristic of the processed images, using said first metadata, and a factorisation of the second metadata said factorisation consisting in applying a joint processing of second metadata obtained for processed images from at least two distinct video surveillance cameras, in order to perform at least one situation analysis associated with the video streams, and a transmission of the situation analysis result (A j) by the remote computer system to a control centre, the method further including a prior step of determining said first sub-network of neurons (R 1) and said second sub-network of neurons (R 2) from from a deep neural network comprising a plurality of hierarchical depth levels, the said determination comprising the calculation of a deciphering depth level, the first neural sub-network (R 1) implementing the depth levels of said neural network lower than said slicing depth level, the second sub-network of neurons (R 2) implementing the depth levels of said neural network higher than said slicing depth level, in which, for a plurality of tasks to be executed by said deep neural network, said calculation of a slicing depth level is performed, for each video surveillance camera, as a function of calculation capacities of said at least one processor integrated into said video surveillance camera, of the computing capacities of said remote computer system, and of at least one performance criterion calculated for each task to be executed, the computing capacity being estimated by a task execution time, the calculation of a slicing depth level also being a function of a processing frequency associated with each task to be executed.