A surveillance method using multi-dimensional sensor data, for use in a surveillance system, wherein the surveillance system comprises a plurality of sensors installed within a scene, and the plurality of sensors are classified into a plurality of types, the surveillance method comprising: obtaining sensor data corresponding to each type about the scene using the plurality of sensors; performing a local-object process on the sensor data corresponding to each type to generate local-object-feature information for each type; performing a global-object process according to the local-object-feature information for each type to generate global-object-feature information; and performing a global-object recognition process on the global-object-feature information to generate a global-recognition result, wherein the surveillance method is characterized in that: the sensors comprise: cameras and microphones, and the step of performing the local-object process on the sensor data corresponding to each type to generate local-object-feature information comprises: performing a local-object detection and correspondence process on the sensor data corresponding to each type to obtain a local-object-identifier (LOID) list and a corresponding local-rough-feature set (LRFS) for each type; performing a local-feature-extraction-and-fusion process on the sensor data corresponding to each type according to the LOID list and the corresponding LRFS for each type to obtain a plurality of local-detail-feature sets (LDFS), and fusing the plurality of LDFSs into a local-fusion feature corresponding to each type; and inputting the local-fusion feature corresponding to each type into a local-object-recognition model to obtain a local-identity-identifier (LIID) list corresponding to each type, wherein the LIID list for each type comprises one or more local objects corresponding to each type, and each local object has a corresponding local-object identifier (LOID), wherein the LRFS comprises direction, distance, and roughness of each type of local object. obtaining sensor data corresponding to each type about the scene using the plurality of sensors; performing a local-object process on the sensor data corresponding to each type to generate local-object-feature information for each type; performing a global-object process according to the local-object-feature information for each type to generate global-object-feature information; and performing a global-object recognition process on the global-object-feature information to generate a global-recognition result, wherein the surveillance method is characterized in that: the sensors comprise: cameras and microphones, and the step of performing the local-object process on the sensor data corresponding to each type to generate local-object-feature information comprises: performing a local-object detection and correspondence process on the sensor data corresponding to each type to obtain a local-object-identifier (LOID) list and a corresponding local-rough-feature set (LRFS) for each type; performing a local-feature-extraction-and-fusion process on the sensor data corresponding to each type according to the LOID list and the corresponding LRFS for each type to obtain a plurality of local-detail-feature sets (LDFS), and fusing the plurality of LDFSs into a local-fusion feature corresponding to each type; and inputting the local-fusion feature corresponding to each type into a local-object-recognition model to obtain a local-identity-identifier (LIID) list corresponding to each type, wherein the LIID list for each type comprises one or more local objects corresponding to each type, and each local object has a corresponding local-object identifier (LOID), wherein the LRFS comprises direction, distance, and roughness of each type of local object. performing a local-object detection and correspondence process on the sensor data corresponding to each type to obtain a local-object-identifier (LOID) list and a corresponding local-rough-feature set (LRFS) for each type; performing a local-feature-extraction-and-fusion process on the sensor data corresponding to each type according to the LOID list and the corresponding LRFS for each type to obtain a plurality of local-detail-feature sets (LDFS), and fusing the plurality of LDFSs into a local-fusion feature corresponding to each type; and inputting the local-fusion feature corresponding to each type into a local-object-recognition model to obtain a local-identity-identifier (LIID) list corresponding to each type, wherein the LIID list for each type comprises one or more local objects corresponding to each type, and each local object has a corresponding local-object identifier (LOID), wherein the LRFS comprises direction, distance, and roughness of each type of local object.