Abstract: Video devices are widely used in public areas, smart transportation, industrial production and many other fields. Video data has typical characteristics of huge volume, fast speed, sparse value and completely unstructured data. To achieve higher processing performance for video big data, a parallel processing approach based on Spark Streaming is proposed. A parallel processing framework based on Spark Streaming is designed. Especially, parallel strategies for inter-frame independent algorithms and inter-frame correlation algorithms are given in detail. The former strategy maps the de-redundant video frames to different nodes with data parallelism, and the latter maps the operators of the algorithm to different nodes based on the dependency relationship. The parallel processing approach is evaluated with real video big data. A parallel detection algorithm for elevator passenger number and a parallel detection algorithm for elevator door anomalies are designed. When the number of nodes increases to 16, the speedup of the elevator passenger number detection algorithm is 615%, and the speedup of elevator door anomaly detection is 253%.

Key words: video big data, parallel processing strategy, inter-frame correlation, inter-frame independence