The increasing availability of low-cost battery-operated wireless cameras has motivated the deployment of large-scale Wireless Multimedia Sensor Networks (WMSNs) which can be leveraged for gathering disparate views of events from multiple perspectives. Such multi-perspective coverage not only provides better visual knowledge about the events but also helps reduce occlusions in many critical applications. Different than traditional k-coverage in Wireless Sensor Networks (WSNs), multi-perspective coverage computation considers the orientation of cameras in addition to their locations. In this paper, we first introduce a new metric which can measure multi-perspective coverage for a particular region from a given number of perspectives. Using this metric, we then propose camera placement techniques based on binary integer programming and heuristics to achieve full multi-perspective coverage with the least camera count. Finally, to be used as a baseline, we come up with a formula which can analytically compute the multi-perspective coverage for a given network of randomly placed cameras in a certain region. We evaluated the performance of these camera placement approaches (e.g., integer programming, heuristic and random) in terms of coverage and number of cameras needed under different number of perspectives.