This paper presents a vision system which allows real-time recognition of temporal swimming activities and the detection of drowning incident. Operating with a set of techniques, the developed system focuses on two fundamental issues: i) way to analyze temporal behavior and ii) way to incorporate expert knowledge. To perform the recognition of different behaviors, data fusion and Hidden Markov Model (HMM) techniques are implemented. A polynomial classifier is introduced to deal with noisy foreground descriptors caused by poor resolution and sensory noise. It addresses the nonlinear interactions among different dimensions of foreground descriptors while preserving the linear estimation property. HMM is used to model the state transition process that yields a simple and efficient probabilistic inference engine. This work reports the results of extensive on-site experiments carried out. The results demonstrate reasonably good performance yielded, specifically, in terms of false alarm rates and detection of genuine water crises.