Museum Personalized : The impact of floor staff on an exhibition - A holistic approach

The current paper presents a study conducted at The National Museum of Science and Technology in Stockholm to investigate the exhibition “Antarctica – that’s cool” from its first concept to the first workshop that is held in the exhibition. The focus is on the influence of floor staff on an exhibition and workshops as learning facilities in museums. Findings, based on visitor observation and the exhibition building process, go into the characteristics of low-budget productions and discuss the importance of staff on the exhibition floor for museums as life-long learning facilities. The holistic approach of the study provides deep insights into the complex interplay of visitors, staff and exhibitions. The results can be used for future exhibition building processes and educational programs in museums and should strengthen the museum’s position as life-long learning facility in nowadays society.

Tapping into Floor Staff: Using the knowledge of floor staff to conduct formative evaluations of exhibits in a Canadian science centre

Most science centres in Canada employ science-educated floor staff to motivate visitorsto have fun while enhancing the educational reach of the exhibits. Although bright andsensitive to visitors’ needs, floor staff are rarely consulted in the planning,implementation, and modification phases of an exhibit. Instead, many developmentteams rely on costly third-party evaluations or skip the front-end and formativeevaluations all together, leading to costly errors that could have been avoided. This studywill seek to reveal a correlation between floor staff’s perception of visitors’ interactionswith an exhibit and visitors’ actual experiences. If a correlation exists, a recommendationcould be made to encourage planning teams to include floor staff in the formative andsummative evaluations of an exhibit. This is especially relevant to science centres withlimited budgets and for whom a divide exists between floor staff and management.In this study, a formative evaluation of one exhibit was conducted, measuring both floorstaff’s perceptions of the visitor experience and visitors’ own perceptions of the exhibit.Floor staff were then trained on visitor evaluation methods. A week later, floor staff andvisitors were surveyed a second time on a different exhibit to determine whether anincrease in accuracy existed.The training session increased the specificity of the motivation and comprehensionresponses and the enthusiasm of the staff, but not their ability to predict observedbehaviours with respect to ergonomics, learning indicators, holding power, and successrates. The results revealed that although floor staff underestimated visitors’ success ratesat the exhibits, staff accurately predicted visitors’ behaviours with respect to holdingpower, ergonomics, learning indicators, motivation and comprehension, both before andafter the staff training.

Real time video segmentation for recognising paint marks on bad wooden railway sleepers

Wooden railway sleeper inspections in Sweden are currently performed manually by a human operator; such inspections are based on visual analysis. Machine vision based approach has been done to emulate the visual abilities of human operator to enable automation of the process. Through this process bad sleepers are identified, and a spot is marked on it with specific color (blue in the current case) on the rail so that the maintenance operators are able to identify the spot and replace the sleeper. The motive of this thesis is to help the operators to identify those sleepers which are marked by color (spots), using an “Intelligent Vehicle” which is capable of running on the track. Capturing video while running on the track and segmenting the object of interest (spot) through this vehicle; we can automate this work and minimize the human intuitions. The video acquisition process depends on camera position and source light to obtain fine brightness in acquisition, we have tested 4 different types of combinations (camera position and source light) here to record the video and test the validity of proposed method. A sequence of real time rail frames are extracted from these videos and further processing (depending upon the data acquisition process) is done to identify the spots. After identification of spot each frame is divided in to 9 regions to know the particular region where the spot lies to avoid overlapping with noise, and so on. The proposed method will generate the information regarding in which region the spot lies, based on nine regions in each frame. From the generated results we have made some classification regarding data collection techniques, efficiency, time and speed. In this report, extensive experiments using image sequences from particular camera are reported and the experiments were done using intelligent vehicle as well as test vehicle and the results shows that we have achieved 95% success in identifying the spots when we use video as it is, in other method were we can skip some frames in pre-processing to increase the speed of video but the segmentation results we reduced to 85% and the time was very less compared to previous one. This shows the validity of proposed method in identification of spots lying on wooden railway sleepers where we can compromise between time and efficiency to get the desired result.